In the 15 years since Wachter and Goldman coined the term hospitalists, the specialty of Hospital Medicine grew faster than any other in the history of American medicine.1 The early drivers for growth were largely economic: There were significant reductions in resource use, with a 13% decrease in hospital costs and a 16% decrease in hospital lengths of stay (LOS).2 Hospitalist clinician‐educators increased the satisfaction of residents and medical students in academic settings.2 Patient satisfaction and hospital mortality did not suffer.2

Recent growth of Hospital Medicine revolves around 3 drivers: 1) improving quality and safety of hospitalized patientsowing in large part to the Institute of Medicine's 2 compelling reports, To Err Is Human3 and Crossing the Quality Chasm4; 2) hospitalist and specialist (surgeon) comanagement; and 3) the effects of duty hours restrictions imposed by the Accreditation Council for Graduate Medical Education affecting United States (US) teaching hospitals.5

In this issue of the Journal of Hospital Medicine, Shu and colleagues6 report on the performance of a hospitalist program in Taiwan. To the best of our knowledge, this report from Asia is the first published report of a successful hospitalist model with measurable patient outcomes outside of North America. Specifically, over a year, the authors found that patients admitted by hospitalists had a shorter LOS and lower cost per case, with no difference in in‐hospital mortality and 30‐day readmission. These results were obtained despite the fact that the cohort of patients admitted to the hospitalist team was older, sicker, and had worse functional capacity. Additionally, the patients admitted to the hospitalist team, and who died during hospitalization, were more likely to have a do‐not‐resuscitate (DNR) order signed, when compared with those patients admitted to the general internal medicine teaching service. Comparing LOS with North America may be problematic. As Shu and colleagues6 point out, there are cultural and economic issues that affect the behavior of patients and physicians in Taiwan.

The healthcare system in Taiwan has similarities to the healthcare systems in the United Kingdom (UK) and the US. In 1995, Taiwan implemented a national health insurance system. The UK has had a National Health Service for many years that provides most services for free. The Taiwanese system requires modest copayments for services. The implementation of the national health insurance system in Taiwan increased healthcare access from 57% of the population to 98%.7 The increase in insurance across the population with modest copayments has made it possible for a larger percentage of the population to access the healthcare system.7 According to the authors, this has resulted in increased hospital admissions (35% in the decade since the introduction of national health insurance), resulting in a shortage of Hospital Medicine physicians and hospital beds.7 Compounding the stressors on this system is that the diagnosis related group (DRG) reimbursement model, similar to the American DRG reimbursement model, will soon take effect in Taiwan. As a result, our colleagues in Taiwan are experiencing issues commonly faced by mature hospitalist programs in the US: increased needs in efficiency to improve patient flow and decrease emergency room overcrowding and LOS; and concerns with safe discharges of high‐risk patients while ensuring outpatient follow‐up. This is a scenario with which US hospitalists are all too familiar.

The next step for Taiwan might be to implement a culturally specific patient education program regarding the discharge process. The first step would be a needs assessment survey of patients in Taiwan, inquiring about concerns regarding readiness for discharge. They might inquire about patient beliefs regarding understanding indications for inpatient hospitalization versus discharge to home, home with home services, or skilled nursing facilities. They might be able to drill down to the root cause of refusal to be discharged home. These data could help our colleagues in Taiwan create their own discharge program to drive down LOS closer to that of the US and other Western countries, in order to reap financial benefits and improve resource utilization.

What do we know about the growth of Hospital Medicine around the world? The Society of Hospital Medicine (SHM) reports international members from 26 countries around the world. In North America, SHM members are found in the US, Canada, and Bermuda. In Europe, SHM members are found in England, Ireland, Scotland, Spain, Belgium, Portugal, Italy, and Germany. In South America, SHM members are found in Brazil, Chile, Colombia, and Argentina. In Asia and the Middle East, SHM has members in Saudi Arabia, Israel, United Arab Emirates, Pakistan, Japan, China, the Philippines, and Singapore. In Oceania, SHM has members in Australia, and New Zealand. In Africa, 1 SHM member is from Nigeria (Society of Hospital Medicine Data, 2011). In fact, the International Hospitalists Section of SHM is 1 of only 2 sections that the Society recognizes.

Hospitalists are organizing themselves abroad as well. In Canada, the Canadian Society of Hospital Medicine was founded in 2001 and has had 8 national conferences to date.8 There are roughly 1,000 Canadian hospitalists (Wilton D, personal communication, 2011). Whereas most US hospitalists are internists or pediatricians, in Canada, most hospitalists are family physicians. In the US, hospitalists are more likely to perform the following services: consultation, intensive care unit patient care, rapid response team service, surgical comanagement, and evening on‐site coverage. Canadian hospitalists are more likely to provide pediatric care and psychiatry inpatient comanagement.9

In the UK, the professional organization of physicians most similar to US hospitalists, acute physicians, is called SAM (The Society for Acute Medicine). It was founded in 2000.10 In the UK, general practitioners (GPs) never care for inpatients; at the time, GPs referred all admissions to organ‐specific specialists (eg, cardiologists). Acute medicine was created due to the realization that medical inpatients were too complex to have specialists managing them. Training programs were set up circa 2003 to create this specialty and address this need. Acute physicians staff geographically localized acute medicine units near emergency departments. These patients stay 1 to 3 days in an effort to concentrate services and resources to these patients, to prevent longer stays once fully admitted (Smith R, personal communication, April 23, 2011). Acute medicine units in the UK, Ireland, and Australia have led to positive benefits on patient outcomes. A review article by Scott and colleagues revealed reductions in LOS, inpatient mortality, and emergency department LOS, without increased 30‐day readmission rates. They found increased staff and patient satisfaction, and more medical patients discharged directly to home from acute medical units.11 The development of acute medicine in Australia and New Zealand began around 2005 and derives from the geographic localization of the UK model. Whereas the UK model has a focus on the first 72 hours of hospitalization, the model in Australia and New Zealand is more similar to the US model of following patients through their entire admission.12 Unlike the UK, Australia does not have dedicated acute medicine training programs.

PASHA, the Pan‐American Society of Hospitalists, is a loose affiliation of hospitalists largely in South America, linking with their North American colleagues. PASHA grew out of SOBRAMH, Sociedade Brasileira de Medicina Hospitalarthe first Hospital Medicine Society in South America, tracing its roots to 2004. To date, PASHA has had 1 international conference, but there have been 2 national conferences each in Brazil and Chile, and 1 in Colombia. The concept and advantages of Hospital Medicine have been presented at a conference in Panama. Argentina has its first Hospital Medicine Congress scheduled for September 2011, in concert with PASHA.

Two Hospital Medicine programs abroad deserve special mention. Both started in 2005 and have instituted the full hospitalist package, including multiple evidence‐based order sets at both sites (eg, deep vein thrombosis [DVT] prophylaxis and hyperglycemia management). At the Pontificia Universidad Catlica in Santiago, Chile, they have been awarded national grants to study hyperglycemia in hospitalized patients, and they have sent their faculty to the US for additional training in patient safety, quality improvement, leadership, and medical informatics. They have succeeded in decreasing LOS and improved the exam grades of their learners. Their faculty has published in national journals and is now beginning to submit their work for publication in US‐based journals (Rojas L, personal communication, April 22, 2011). The Clnica Universidad de Navarra (CUN) in Pamplona, Spain is a Joint Commission certified facility with a full electronic medical record. Hospitalists there are looking at ways in which hospitalist‐staffed intermediate care units can benefit patient outcomes. Additionally, they have comanagement arrangements with nearly all surgical subspecialties. The Management of the Hospitalized Patient symposium was organized by CUN hospitalists in 2007the first Hospital Medicine Congress, to our knowledge, in continental Europe. At any one time, 30% of all residents in all specialties rotate with CUN hospitalists (Lucena F, personal communication, April 22, 2011).

The specialty of Hospital Medicine is truly global. Our colleagues around the world employing the hospitalist model of care are now producing outcomes similar to the published models in North America and to the acute medicine models in Europe and Australia. According to the Society of Hospital Medicine, there are over 30,000 hospitalists in the US. There could be well over 50,000 hospitalists around the world. In 5 years, the world may have 100,000 hospitalists. The same drivers are fueling the growth of Hospital Medicine around the world. The evidence is building that the hospitalist model of care has financial and quality benefits that transcend borders. We forecast that the hospitalist model of care will become an increasingly larger part of the solution around the world to fix these international healthcare systems.

In the 15 years since Wachter and Goldman coined the term hospitalists, the specialty of Hospital Medicine grew faster than any other in the history of American medicine.1 The early drivers for growth were largely economic: There were significant reductions in resource use, with a 13% decrease in hospital costs and a 16% decrease in hospital lengths of stay (LOS).2 Hospitalist clinician‐educators increased the satisfaction of residents and medical students in academic settings.2 Patient satisfaction and hospital mortality did not suffer.2

Recent growth of Hospital Medicine revolves around 3 drivers: 1) improving quality and safety of hospitalized patientsowing in large part to the Institute of Medicine's 2 compelling reports, To Err Is Human3 and Crossing the Quality Chasm4; 2) hospitalist and specialist (surgeon) comanagement; and 3) the effects of duty hours restrictions imposed by the Accreditation Council for Graduate Medical Education affecting United States (US) teaching hospitals.5

In this issue of the Journal of Hospital Medicine, Shu and colleagues6 report on the performance of a hospitalist program in Taiwan. To the best of our knowledge, this report from Asia is the first published report of a successful hospitalist model with measurable patient outcomes outside of North America. Specifically, over a year, the authors found that patients admitted by hospitalists had a shorter LOS and lower cost per case, with no difference in in‐hospital mortality and 30‐day readmission. These results were obtained despite the fact that the cohort of patients admitted to the hospitalist team was older, sicker, and had worse functional capacity. Additionally, the patients admitted to the hospitalist team, and who died during hospitalization, were more likely to have a do‐not‐resuscitate (DNR) order signed, when compared with those patients admitted to the general internal medicine teaching service. Comparing LOS with North America may be problematic. As Shu and colleagues6 point out, there are cultural and economic issues that affect the behavior of patients and physicians in Taiwan.

The healthcare system in Taiwan has similarities to the healthcare systems in the United Kingdom (UK) and the US. In 1995, Taiwan implemented a national health insurance system. The UK has had a National Health Service for many years that provides most services for free. The Taiwanese system requires modest copayments for services. The implementation of the national health insurance system in Taiwan increased healthcare access from 57% of the population to 98%.7 The increase in insurance across the population with modest copayments has made it possible for a larger percentage of the population to access the healthcare system.7 According to the authors, this has resulted in increased hospital admissions (35% in the decade since the introduction of national health insurance), resulting in a shortage of Hospital Medicine physicians and hospital beds.7 Compounding the stressors on this system is that the diagnosis related group (DRG) reimbursement model, similar to the American DRG reimbursement model, will soon take effect in Taiwan. As a result, our colleagues in Taiwan are experiencing issues commonly faced by mature hospitalist programs in the US: increased needs in efficiency to improve patient flow and decrease emergency room overcrowding and LOS; and concerns with safe discharges of high‐risk patients while ensuring outpatient follow‐up. This is a scenario with which US hospitalists are all too familiar.

The next step for Taiwan might be to implement a culturally specific patient education program regarding the discharge process. The first step would be a needs assessment survey of patients in Taiwan, inquiring about concerns regarding readiness for discharge. They might inquire about patient beliefs regarding understanding indications for inpatient hospitalization versus discharge to home, home with home services, or skilled nursing facilities. They might be able to drill down to the root cause of refusal to be discharged home. These data could help our colleagues in Taiwan create their own discharge program to drive down LOS closer to that of the US and other Western countries, in order to reap financial benefits and improve resource utilization.

What do we know about the growth of Hospital Medicine around the world? The Society of Hospital Medicine (SHM) reports international members from 26 countries around the world. In North America, SHM members are found in the US, Canada, and Bermuda. In Europe, SHM members are found in England, Ireland, Scotland, Spain, Belgium, Portugal, Italy, and Germany. In South America, SHM members are found in Brazil, Chile, Colombia, and Argentina. In Asia and the Middle East, SHM has members in Saudi Arabia, Israel, United Arab Emirates, Pakistan, Japan, China, the Philippines, and Singapore. In Oceania, SHM has members in Australia, and New Zealand. In Africa, 1 SHM member is from Nigeria (Society of Hospital Medicine Data, 2011). In fact, the International Hospitalists Section of SHM is 1 of only 2 sections that the Society recognizes.

Hospitalists are organizing themselves abroad as well. In Canada, the Canadian Society of Hospital Medicine was founded in 2001 and has had 8 national conferences to date.8 There are roughly 1,000 Canadian hospitalists (Wilton D, personal communication, 2011). Whereas most US hospitalists are internists or pediatricians, in Canada, most hospitalists are family physicians. In the US, hospitalists are more likely to perform the following services: consultation, intensive care unit patient care, rapid response team service, surgical comanagement, and evening on‐site coverage. Canadian hospitalists are more likely to provide pediatric care and psychiatry inpatient comanagement.9

In the UK, the professional organization of physicians most similar to US hospitalists, acute physicians, is called SAM (The Society for Acute Medicine). It was founded in 2000.10 In the UK, general practitioners (GPs) never care for inpatients; at the time, GPs referred all admissions to organ‐specific specialists (eg, cardiologists). Acute medicine was created due to the realization that medical inpatients were too complex to have specialists managing them. Training programs were set up circa 2003 to create this specialty and address this need. Acute physicians staff geographically localized acute medicine units near emergency departments. These patients stay 1 to 3 days in an effort to concentrate services and resources to these patients, to prevent longer stays once fully admitted (Smith R, personal communication, April 23, 2011). Acute medicine units in the UK, Ireland, and Australia have led to positive benefits on patient outcomes. A review article by Scott and colleagues revealed reductions in LOS, inpatient mortality, and emergency department LOS, without increased 30‐day readmission rates. They found increased staff and patient satisfaction, and more medical patients discharged directly to home from acute medical units.11 The development of acute medicine in Australia and New Zealand began around 2005 and derives from the geographic localization of the UK model. Whereas the UK model has a focus on the first 72 hours of hospitalization, the model in Australia and New Zealand is more similar to the US model of following patients through their entire admission.12 Unlike the UK, Australia does not have dedicated acute medicine training programs.

PASHA, the Pan‐American Society of Hospitalists, is a loose affiliation of hospitalists largely in South America, linking with their North American colleagues. PASHA grew out of SOBRAMH, Sociedade Brasileira de Medicina Hospitalarthe first Hospital Medicine Society in South America, tracing its roots to 2004. To date, PASHA has had 1 international conference, but there have been 2 national conferences each in Brazil and Chile, and 1 in Colombia. The concept and advantages of Hospital Medicine have been presented at a conference in Panama. Argentina has its first Hospital Medicine Congress scheduled for September 2011, in concert with PASHA.

Two Hospital Medicine programs abroad deserve special mention. Both started in 2005 and have instituted the full hospitalist package, including multiple evidence‐based order sets at both sites (eg, deep vein thrombosis [DVT] prophylaxis and hyperglycemia management). At the Pontificia Universidad Catlica in Santiago, Chile, they have been awarded national grants to study hyperglycemia in hospitalized patients, and they have sent their faculty to the US for additional training in patient safety, quality improvement, leadership, and medical informatics. They have succeeded in decreasing LOS and improved the exam grades of their learners. Their faculty has published in national journals and is now beginning to submit their work for publication in US‐based journals (Rojas L, personal communication, April 22, 2011). The Clnica Universidad de Navarra (CUN) in Pamplona, Spain is a Joint Commission certified facility with a full electronic medical record. Hospitalists there are looking at ways in which hospitalist‐staffed intermediate care units can benefit patient outcomes. Additionally, they have comanagement arrangements with nearly all surgical subspecialties. The Management of the Hospitalized Patient symposium was organized by CUN hospitalists in 2007the first Hospital Medicine Congress, to our knowledge, in continental Europe. At any one time, 30% of all residents in all specialties rotate with CUN hospitalists (Lucena F, personal communication, April 22, 2011).

The specialty of Hospital Medicine is truly global. Our colleagues around the world employing the hospitalist model of care are now producing outcomes similar to the published models in North America and to the acute medicine models in Europe and Australia. According to the Society of Hospital Medicine, there are over 30,000 hospitalists in the US. There could be well over 50,000 hospitalists around the world. In 5 years, the world may have 100,000 hospitalists. The same drivers are fueling the growth of Hospital Medicine around the world. The evidence is building that the hospitalist model of care has financial and quality benefits that transcend borders. We forecast that the hospitalist model of care will become an increasingly larger part of the solution around the world to fix these international healthcare systems.

In the 15 years since Wachter and Goldman coined the term hospitalists, the specialty of Hospital Medicine grew faster than any other in the history of American medicine.1 The early drivers for growth were largely economic: There were significant reductions in resource use, with a 13% decrease in hospital costs and a 16% decrease in hospital lengths of stay (LOS).2 Hospitalist clinician‐educators increased the satisfaction of residents and medical students in academic settings.2 Patient satisfaction and hospital mortality did not suffer.2

Recent growth of Hospital Medicine revolves around 3 drivers: 1) improving quality and safety of hospitalized patientsowing in large part to the Institute of Medicine's 2 compelling reports, To Err Is Human3 and Crossing the Quality Chasm4; 2) hospitalist and specialist (surgeon) comanagement; and 3) the effects of duty hours restrictions imposed by the Accreditation Council for Graduate Medical Education affecting United States (US) teaching hospitals.5

In this issue of the Journal of Hospital Medicine, Shu and colleagues6 report on the performance of a hospitalist program in Taiwan. To the best of our knowledge, this report from Asia is the first published report of a successful hospitalist model with measurable patient outcomes outside of North America. Specifically, over a year, the authors found that patients admitted by hospitalists had a shorter LOS and lower cost per case, with no difference in in‐hospital mortality and 30‐day readmission. These results were obtained despite the fact that the cohort of patients admitted to the hospitalist team was older, sicker, and had worse functional capacity. Additionally, the patients admitted to the hospitalist team, and who died during hospitalization, were more likely to have a do‐not‐resuscitate (DNR) order signed, when compared with those patients admitted to the general internal medicine teaching service. Comparing LOS with North America may be problematic. As Shu and colleagues6 point out, there are cultural and economic issues that affect the behavior of patients and physicians in Taiwan.

The healthcare system in Taiwan has similarities to the healthcare systems in the United Kingdom (UK) and the US. In 1995, Taiwan implemented a national health insurance system. The UK has had a National Health Service for many years that provides most services for free. The Taiwanese system requires modest copayments for services. The implementation of the national health insurance system in Taiwan increased healthcare access from 57% of the population to 98%.7 The increase in insurance across the population with modest copayments has made it possible for a larger percentage of the population to access the healthcare system.7 According to the authors, this has resulted in increased hospital admissions (35% in the decade since the introduction of national health insurance), resulting in a shortage of Hospital Medicine physicians and hospital beds.7 Compounding the stressors on this system is that the diagnosis related group (DRG) reimbursement model, similar to the American DRG reimbursement model, will soon take effect in Taiwan. As a result, our colleagues in Taiwan are experiencing issues commonly faced by mature hospitalist programs in the US: increased needs in efficiency to improve patient flow and decrease emergency room overcrowding and LOS; and concerns with safe discharges of high‐risk patients while ensuring outpatient follow‐up. This is a scenario with which US hospitalists are all too familiar.

The next step for Taiwan might be to implement a culturally specific patient education program regarding the discharge process. The first step would be a needs assessment survey of patients in Taiwan, inquiring about concerns regarding readiness for discharge. They might inquire about patient beliefs regarding understanding indications for inpatient hospitalization versus discharge to home, home with home services, or skilled nursing facilities. They might be able to drill down to the root cause of refusal to be discharged home. These data could help our colleagues in Taiwan create their own discharge program to drive down LOS closer to that of the US and other Western countries, in order to reap financial benefits and improve resource utilization.

What do we know about the growth of Hospital Medicine around the world? The Society of Hospital Medicine (SHM) reports international members from 26 countries around the world. In North America, SHM members are found in the US, Canada, and Bermuda. In Europe, SHM members are found in England, Ireland, Scotland, Spain, Belgium, Portugal, Italy, and Germany. In South America, SHM members are found in Brazil, Chile, Colombia, and Argentina. In Asia and the Middle East, SHM has members in Saudi Arabia, Israel, United Arab Emirates, Pakistan, Japan, China, the Philippines, and Singapore. In Oceania, SHM has members in Australia, and New Zealand. In Africa, 1 SHM member is from Nigeria (Society of Hospital Medicine Data, 2011). In fact, the International Hospitalists Section of SHM is 1 of only 2 sections that the Society recognizes.

Hospitalists are organizing themselves abroad as well. In Canada, the Canadian Society of Hospital Medicine was founded in 2001 and has had 8 national conferences to date.8 There are roughly 1,000 Canadian hospitalists (Wilton D, personal communication, 2011). Whereas most US hospitalists are internists or pediatricians, in Canada, most hospitalists are family physicians. In the US, hospitalists are more likely to perform the following services: consultation, intensive care unit patient care, rapid response team service, surgical comanagement, and evening on‐site coverage. Canadian hospitalists are more likely to provide pediatric care and psychiatry inpatient comanagement.9

In the UK, the professional organization of physicians most similar to US hospitalists, acute physicians, is called SAM (The Society for Acute Medicine). It was founded in 2000.10 In the UK, general practitioners (GPs) never care for inpatients; at the time, GPs referred all admissions to organ‐specific specialists (eg, cardiologists). Acute medicine was created due to the realization that medical inpatients were too complex to have specialists managing them. Training programs were set up circa 2003 to create this specialty and address this need. Acute physicians staff geographically localized acute medicine units near emergency departments. These patients stay 1 to 3 days in an effort to concentrate services and resources to these patients, to prevent longer stays once fully admitted (Smith R, personal communication, April 23, 2011). Acute medicine units in the UK, Ireland, and Australia have led to positive benefits on patient outcomes. A review article by Scott and colleagues revealed reductions in LOS, inpatient mortality, and emergency department LOS, without increased 30‐day readmission rates. They found increased staff and patient satisfaction, and more medical patients discharged directly to home from acute medical units.11 The development of acute medicine in Australia and New Zealand began around 2005 and derives from the geographic localization of the UK model. Whereas the UK model has a focus on the first 72 hours of hospitalization, the model in Australia and New Zealand is more similar to the US model of following patients through their entire admission.12 Unlike the UK, Australia does not have dedicated acute medicine training programs.

PASHA, the Pan‐American Society of Hospitalists, is a loose affiliation of hospitalists largely in South America, linking with their North American colleagues. PASHA grew out of SOBRAMH, Sociedade Brasileira de Medicina Hospitalarthe first Hospital Medicine Society in South America, tracing its roots to 2004. To date, PASHA has had 1 international conference, but there have been 2 national conferences each in Brazil and Chile, and 1 in Colombia. The concept and advantages of Hospital Medicine have been presented at a conference in Panama. Argentina has its first Hospital Medicine Congress scheduled for September 2011, in concert with PASHA.

Two Hospital Medicine programs abroad deserve special mention. Both started in 2005 and have instituted the full hospitalist package, including multiple evidence‐based order sets at both sites (eg, deep vein thrombosis [DVT] prophylaxis and hyperglycemia management). At the Pontificia Universidad Catlica in Santiago, Chile, they have been awarded national grants to study hyperglycemia in hospitalized patients, and they have sent their faculty to the US for additional training in patient safety, quality improvement, leadership, and medical informatics. They have succeeded in decreasing LOS and improved the exam grades of their learners. Their faculty has published in national journals and is now beginning to submit their work for publication in US‐based journals (Rojas L, personal communication, April 22, 2011). The Clnica Universidad de Navarra (CUN) in Pamplona, Spain is a Joint Commission certified facility with a full electronic medical record. Hospitalists there are looking at ways in which hospitalist‐staffed intermediate care units can benefit patient outcomes. Additionally, they have comanagement arrangements with nearly all surgical subspecialties. The Management of the Hospitalized Patient symposium was organized by CUN hospitalists in 2007the first Hospital Medicine Congress, to our knowledge, in continental Europe. At any one time, 30% of all residents in all specialties rotate with CUN hospitalists (Lucena F, personal communication, April 22, 2011).

The specialty of Hospital Medicine is truly global. Our colleagues around the world employing the hospitalist model of care are now producing outcomes similar to the published models in North America and to the acute medicine models in Europe and Australia. According to the Society of Hospital Medicine, there are over 30,000 hospitalists in the US. There could be well over 50,000 hospitalists around the world. In 5 years, the world may have 100,000 hospitalists. The same drivers are fueling the growth of Hospital Medicine around the world. The evidence is building that the hospitalist model of care has financial and quality benefits that transcend borders. We forecast that the hospitalist model of care will become an increasingly larger part of the solution around the world to fix these international healthcare systems.

Internal medicine residency training continues to evolve as competency‐based and with education organized around patient care.13 Making the patient the center of resident education provides an opportunity for experiential learning in which learning can be organized around the clinical conditions that residents encounter. Despite the renewed emphasis on using patient experience as the basis for residency education, little is known regarding what specific diagnostic conditions are seen by internal medicine residents throughout their training. Attempts have been made to quantify resident clinical experience in various fields, using approaches such as review of medical records, case logs, and prescription profiles, but to date, we lack systematic methods to obtain clinical experience data for internal medicine residents.47

While residency curricula in internal medicine typically outlines specific rotations in various clinical areas such as general medical wards, cardiology services, and intensive care units, time spent on such rotations does not necessarily provide quantitative data on the actual clinical conditions that residents encounter, nor does it ensure consistent clinical experience between residents. It is plausible that there may be substantial variability in clinical experience between residents within the same program, and that the overall spectrum of clinical disorders seen by residents in a program may or may not be consistent with a desired optimum, though this is yet to be defined.

If residency education in internal medicine is to progressively incorporate more experiential learning, detailed knowledge of the clinical conditions seen by residents should be useful, not only for overall curriculum design, but this might also allow for various educational interventions to be made when there are variations in clinical experience between residents. Our program has been interested in the application of electronic resources for the improvement of patient care, such as through the handoff process and the use of personal digital assistants.8 We previously did a small analysis of clinical conditions seen by residents through non‐International Classification of Diseases, Ninth Revision (ICD‐9)‐based data they entered onto personal digital assistants. This suggested to us that electronic resources used by residents might serve as a venue by which they could enter diagnostic information which we could use to generate a more detailed analysis of the clinical conditions that they see. Here we describe a method by which we have attempted to quantify resident clinical experience in internal medicine using a modification of an electronic handoff system.

METHODS

The study was conducted within the Internal Medicine Residency Program at the Long Island Jewish Medical Center in New Hyde Park, New York, part of the North ShoreLong Island Jewish Health System, and was approved by the Institutional Review Board. This work was carried out as part of our participation in the Educational Innovation Project of the Residency Review Committee for Internal Medicine. A central objective of our proposal was to develop a method to assess residents' clinical experience on an individual and an aggregate basis. A group of faculty and residents in our residency program developed an electronic handoff tool which residents use for rapid access to key clinical data for their patients and for the handoff of clinical information for on call coverage. This handoff tool was developed with the technical assistance of MedTech Notes LLC which owns Patient Data Transfer System (PDTS) HandOff Note. We modified the handoff tool to include a section in which residents were required to enter a primary diagnosis for each of their patients (a hard stop design). We chose to use the ICD‐9 system for standardization and created two methods to select the code: 1) an organ system‐based dropdown list containing frequently used codes and 2) a search box allowing for searching of the complete ICD‐9 database. For the organ‐based dropdown list, selection of that organ system would reveal a brief list of frequently used codes to make it easier for residents to find them. Prior to using the handoff tool with the ICD‐9based primary diagnosis coding system, training sessions with the residents were conducted by 3 of the investigators along with 3 chief medical residents. These sessions included training not only in technical aspects of how to find diagnosis codes, but also how to make decisions regarding what the primary diagnosis should be. We also instructed our postgraduate year (PGY)‐1s to update their diagnostic selections during the course of the hospital stay.

Each data point represents a resident caring for a patient with a specific diagnostic entity, and is counted once for that resident's period of taking care of that patient. Thirty‐three PGY‐1s were studied and, on the internal medicine service, they were supervised by either hospitalist faculty or voluntary faculty in comparable proportions. If the patient's care is taken over by another resident, that second resident was also recorded as having had a diagnostic encounter with that patient, hence 1 patient could provide experience with the same diagnostic entity for 1 or more residents. Using this method, the denominator is not patients seen, but residentpatient diagnostic encounters that have taken place. The ICD‐9 diagnostic conditions entered by the residents were grouped using the ICD‐9 system. Individual diagnostic profiles for each resident, as well as an aggregate profile for all residents to reflect the residency program as a whole, were generated. We also carried out an analysis of the ICD‐9 codes entered by 6 consecutive PGY‐1s to assess how the diagnostic spectrum might vary among a small sampling of PGY‐1s. In order to evaluate the accuracy of the residents' diagnostic selections, we carried out a validation assessment using a tool used by the residents' supervising hospitalists (who were the attendings of record for those patients). This was carried out on a subset of patients and could be done at any time during the hospital stay. The hospitalists were asked to review their residents' ICD‐9 codes and indicate whether they agreed or disagreed.

RESULTS

A total of 7562 residentpatient diagnostic encounters were studied from July 1, 2007 through June 1, 2008. Mean patient age was 66 19.4 years. The age distribution is given in Table 1 and reveals that 65% of diagnostic encounters were with patients age 60 years or greater. Twelve housestaff teams were studied, each consisting of 2 PGY‐1s and a supervising PGY‐2 or PGY‐3 resident. All ICD‐9 codes were selected by categorical and preliminary internal medicine PGY‐1s on medical ward and intensive care unit rotations. Residents from other departments doing rotations on the medical service were excluded. A validation assessment of 341 patients indicated 83.3% agreement by the supervising hospitalist with the primary ICD‐9 code selected. ICD‐9 codes were then grouped and categorized using ICD‐9 nomenclature with the distribution provided in Table 2. A wide spectrum of clinical conditions is apparent including symptoms and ill‐defined conditions, circulatory disorders, respiratory disorders, neoplasms, genitourinary disorders, digestive disorders, diseases of the blood/blood forming organs, endocrinologic/nutritional/metabolic/emmmune disorders, and disorders of the skin and subcutaneous tissue, overall accounting for about 86% of resident clinical experience.

We also examined the most common diagnostic conditions within each of these categories. The 3 most common ICD‐9 codes entered by residents within each category are provided in Table 3. Symptoms and ill‐defined conditions represent a sizable portion of resident clinical experience (19.51%). Within this category, the most common conditions were fever; abdominal pain (unspecified site); and chest pain, unspecified. Disorders of the circulatory and respiratory systems were the next most common categories of conditions seen by residents, comprising 18.26% and 12.42%, respectively, of resident clinical experience. Within the category of circulatory disorders, congestive heart failure and acute myocardial infarction were the most common conditions seen; for respiratory disorders, pneumonia, chronic airway obstruction, and asthma were most commonly encountered. In aggregate, symptoms and ill‐defined conditions, and disorders of the circulatory and respiratory systems accounted for 50% of resident clinical experience.

Individual resident clinical experience varied as well. As shown in Table 4, for a group of 6 PGY‐1s, there was substantial variability in the ICD‐9 diagnostic categories. For example, the percentages of codes falling into the cardiovascular disease category ranged from 15.27% to 27.91%, and for respiratory disease ranged from 8.22% to 18.55%. These data suggest that there may be sizable differences in the proportions of various clinical conditions seen by residents over a year of training.

DISCUSSION

Years ago, residency training transitioned from a predominantly bedside experience to a curriculum with a large didactic, non‐bedside component, following parameters defined by organizations such as the Accreditation Council for Graduate Medical Education. Residency training is undergoing substantial change to become competency‐based and to organize learning around patient care experiences.2, 3, 9 The Educational Innovation Project of the Residency Review Committee for Internal Medicine is one such endeavor to help develop new methods by which to accomplish this.1 Effective incorporation of innovative experiential learning methods, based on the core competencies, will require a detailed knowledge of resident clinical experience during the course of their training, yet such data have been sparse in internal medicine. Sequist et al. analyzed data from an electronic medical record to assess resident clinical experience in the outpatient setting.4 Bachur and Nagler have used an electronic patient tracking system to assess the clinical experience of pediatric emergency medicine fellows.5, 6 Most attempts to describe resident clinical experience have relied upon extracting diagnostic information from medical records, case logs, etc, though in another approach, Rohrbaugh et al. reviewed psychiatric resident prescription profiles,7 which might provide some indirect data on clinical experience if applied to internal medicine.

In this study, we attempted to quantify resident clinical experience using resident‐selected ICD‐9 codes, in contrast to other methods that have relied upon medical record review and other resident‐independent approaches. There are various strengths and limitations to this approach. Using the ICD‐9 system provides a number of strengths, a major one being standardization, allowing comparisons between different programs and perhaps even facilitating the development of guidelines for resident clinical experience. In addition, this approach using the ICD‐9 system could be readily implemented at any institution and does not require any specific technology. While we chose to do this through our handoff system, an institution could use any of a variety of other systems to accomplish this. For example, resident‐entered ICD‐9 coding systems could be incorporated into electronic discharge summaries, history and physicals, or progress notes. There may also be some practical benefits to having residents learn how to use the ICD‐9 system at this stage of their careers.

There are limitations to this approach as well. The ICD‐9 system was not intended to be used for medical education purposes. There are features of it that can make finding the best diagnosis difficult, and routes to it may at times seem counterintuitive. While we did not carry out resident surveys, a number of residents anecdotally mentioned that it took time to become comfortable using the system, and it could be challenging at times to find a diagnosis description that best fit what they were looking for. To make diagnosis selection easier, we created an organ system‐based dropdown list in the handoff tool so that when residents select an organ system, another list opens up containing commonly used ICD‐9 codes. This grouping is based on organ system alone and does not necessarily follow the ICD‐9 grouping (in contrast, our reported data in this article are all based on ICD‐9 grouping). A search tool to allow searching the entire ICD‐9 database was also made available on the handoff tool. Other factors that could limit diagnosis code accuracy could be lack of clinical knowledge, and error as a result of pressure to come up with a diagnosis because of the hard stop design of our system, in which residents were required to enter a primary diagnosis, potentially causing alert fatigue. A validation assessment that we carried out revealed fairly good agreement with the specific ICD‐9 codes chosen by the resident, but greater accuracy would be desirable. Further education on diagnosis selection and refinements to the handoff tool should help facilitate this. We are currently addressing this by ongoing education on diagnosis selection and by having the hospitalists share the handoff tool with the residents, allowing them to provide direct feedback on diagnostic selections.

More than 19% of the diagnoses selected by residents fell into the category of symptoms and ill‐defined conditions. This raises a number of potential educational issues. One of those is that if residents do, in fact, encounter such entities at such a high frequency, then the internal medicine curriculum must be structured in such a way as to complement this clinical experience with a comprehensive learning program. However, we must also consider the possibility that, in many such instances, a more definitive diagnosis became evident by the time of discharge and this may not have been reflected in the ICD‐9 code that the resident chose. Hence, the category of symptoms and ill‐defined conditions may actually be somewhat smaller than our findings would suggest.

Many issues will need to be addressed as programs obtain more data on their residents' clinical experience. While there may be many reasons to use the ICD‐9 system for selecting diagnoses including those listed above, the system by which ICD‐9 groups diagnoses might not provide ideal educational information, again as the ICD‐9 system was not designed for this purpose. While in this article we have reported the residents' diagnostic encounters grouped according to the ICD‐9 grouping system to provide an initial standardized description, grouping according to another diagnostic system that is felt to be more educationally meaningful may be preferred.

While one might assume that a higher frequency of exposure to certain clinical conditions should enhance competency, that relationship may not be straightforward in internal medicine. For surgical procedures, there are, in fact, data to show improved outcomes for surgeons with higher operative volumes for those procedures,10 but in internal medicine, we do not have data to demonstrate that competence of a resident caring for a particular condition is enhanced by experience alone. Therefore, as programs obtain more data on clinical experience, it will be important that the focus be kept on quality as opposed to quantity.

Obtaining data on resident clinical experience might greatly facilitate experiential learning approaches. For example, as residents go through training and encounter specific diagnostic conditions, those experiences could be supplemented by various learning innovations to make those experiences more meaningful and, hopefully, more likely to result in the development of competence, though that will require measurement. In our program, for example, we have incorporated an approach using illness scenarios, in that when residents have had a certain level of clinical experience with a given clinical condition, they are assembled in small groups and competency‐based case discussions are carried out with a preceptor. In addition, for those instances in which an individual resident may lack direct clinical experience in a certain area, this might be addressed by interventions to increase their contact with those conditions and/or targeted learning interventions to help develop competence. A resident found to be lacking in clinical experience in a certain area could be assigned to the care of more patients with that condition, or to spending more time in a venue in which that condition is more likely to be encountered. Various learning activities including didactics, case discussions, simulation, self‐directed learning, and others could also be used to compensate for such variability. Furthermore, if a residency program's aggregate clinical experience is divergent from some desirable standard yet to be determined, a detailed knowledge of this could help guide that program's curriculum revision. For example, for residents in a program in which there is relatively low exposure to patients with oncological issues, this could be compensated for by external rotations to achieve more clinical experience in oncology, as well as supplementation of the curriculum with additional learning activities in oncology, which could include small group discussions, self‐directed learning activities, case discussions, and others. While at present there are no defined standards for clinical experience and it remains to be seen if there would be a correlation with development of competence, no such standard would serve a purpose if programs did not have reliable and practical means of clinical experience assessment.

In summary, resident‐selected ICD‐9 codes may be a useful means to obtain data regarding resident clinical experience in internal medicine. Such data may be useful to residency training programs in developing new curricula based on experiential learning.

Internal medicine residency training continues to evolve as competency‐based and with education organized around patient care.13 Making the patient the center of resident education provides an opportunity for experiential learning in which learning can be organized around the clinical conditions that residents encounter. Despite the renewed emphasis on using patient experience as the basis for residency education, little is known regarding what specific diagnostic conditions are seen by internal medicine residents throughout their training. Attempts have been made to quantify resident clinical experience in various fields, using approaches such as review of medical records, case logs, and prescription profiles, but to date, we lack systematic methods to obtain clinical experience data for internal medicine residents.47

While residency curricula in internal medicine typically outlines specific rotations in various clinical areas such as general medical wards, cardiology services, and intensive care units, time spent on such rotations does not necessarily provide quantitative data on the actual clinical conditions that residents encounter, nor does it ensure consistent clinical experience between residents. It is plausible that there may be substantial variability in clinical experience between residents within the same program, and that the overall spectrum of clinical disorders seen by residents in a program may or may not be consistent with a desired optimum, though this is yet to be defined.

If residency education in internal medicine is to progressively incorporate more experiential learning, detailed knowledge of the clinical conditions seen by residents should be useful, not only for overall curriculum design, but this might also allow for various educational interventions to be made when there are variations in clinical experience between residents. Our program has been interested in the application of electronic resources for the improvement of patient care, such as through the handoff process and the use of personal digital assistants.8 We previously did a small analysis of clinical conditions seen by residents through non‐International Classification of Diseases, Ninth Revision (ICD‐9)‐based data they entered onto personal digital assistants. This suggested to us that electronic resources used by residents might serve as a venue by which they could enter diagnostic information which we could use to generate a more detailed analysis of the clinical conditions that they see. Here we describe a method by which we have attempted to quantify resident clinical experience in internal medicine using a modification of an electronic handoff system.

METHODS

The study was conducted within the Internal Medicine Residency Program at the Long Island Jewish Medical Center in New Hyde Park, New York, part of the North ShoreLong Island Jewish Health System, and was approved by the Institutional Review Board. This work was carried out as part of our participation in the Educational Innovation Project of the Residency Review Committee for Internal Medicine. A central objective of our proposal was to develop a method to assess residents' clinical experience on an individual and an aggregate basis. A group of faculty and residents in our residency program developed an electronic handoff tool which residents use for rapid access to key clinical data for their patients and for the handoff of clinical information for on call coverage. This handoff tool was developed with the technical assistance of MedTech Notes LLC which owns Patient Data Transfer System (PDTS) HandOff Note. We modified the handoff tool to include a section in which residents were required to enter a primary diagnosis for each of their patients (a hard stop design). We chose to use the ICD‐9 system for standardization and created two methods to select the code: 1) an organ system‐based dropdown list containing frequently used codes and 2) a search box allowing for searching of the complete ICD‐9 database. For the organ‐based dropdown list, selection of that organ system would reveal a brief list of frequently used codes to make it easier for residents to find them. Prior to using the handoff tool with the ICD‐9based primary diagnosis coding system, training sessions with the residents were conducted by 3 of the investigators along with 3 chief medical residents. These sessions included training not only in technical aspects of how to find diagnosis codes, but also how to make decisions regarding what the primary diagnosis should be. We also instructed our postgraduate year (PGY)‐1s to update their diagnostic selections during the course of the hospital stay.

Each data point represents a resident caring for a patient with a specific diagnostic entity, and is counted once for that resident's period of taking care of that patient. Thirty‐three PGY‐1s were studied and, on the internal medicine service, they were supervised by either hospitalist faculty or voluntary faculty in comparable proportions. If the patient's care is taken over by another resident, that second resident was also recorded as having had a diagnostic encounter with that patient, hence 1 patient could provide experience with the same diagnostic entity for 1 or more residents. Using this method, the denominator is not patients seen, but residentpatient diagnostic encounters that have taken place. The ICD‐9 diagnostic conditions entered by the residents were grouped using the ICD‐9 system. Individual diagnostic profiles for each resident, as well as an aggregate profile for all residents to reflect the residency program as a whole, were generated. We also carried out an analysis of the ICD‐9 codes entered by 6 consecutive PGY‐1s to assess how the diagnostic spectrum might vary among a small sampling of PGY‐1s. In order to evaluate the accuracy of the residents' diagnostic selections, we carried out a validation assessment using a tool used by the residents' supervising hospitalists (who were the attendings of record for those patients). This was carried out on a subset of patients and could be done at any time during the hospital stay. The hospitalists were asked to review their residents' ICD‐9 codes and indicate whether they agreed or disagreed.

RESULTS

A total of 7562 residentpatient diagnostic encounters were studied from July 1, 2007 through June 1, 2008. Mean patient age was 66 19.4 years. The age distribution is given in Table 1 and reveals that 65% of diagnostic encounters were with patients age 60 years or greater. Twelve housestaff teams were studied, each consisting of 2 PGY‐1s and a supervising PGY‐2 or PGY‐3 resident. All ICD‐9 codes were selected by categorical and preliminary internal medicine PGY‐1s on medical ward and intensive care unit rotations. Residents from other departments doing rotations on the medical service were excluded. A validation assessment of 341 patients indicated 83.3% agreement by the supervising hospitalist with the primary ICD‐9 code selected. ICD‐9 codes were then grouped and categorized using ICD‐9 nomenclature with the distribution provided in Table 2. A wide spectrum of clinical conditions is apparent including symptoms and ill‐defined conditions, circulatory disorders, respiratory disorders, neoplasms, genitourinary disorders, digestive disorders, diseases of the blood/blood forming organs, endocrinologic/nutritional/metabolic/emmmune disorders, and disorders of the skin and subcutaneous tissue, overall accounting for about 86% of resident clinical experience.

We also examined the most common diagnostic conditions within each of these categories. The 3 most common ICD‐9 codes entered by residents within each category are provided in Table 3. Symptoms and ill‐defined conditions represent a sizable portion of resident clinical experience (19.51%). Within this category, the most common conditions were fever; abdominal pain (unspecified site); and chest pain, unspecified. Disorders of the circulatory and respiratory systems were the next most common categories of conditions seen by residents, comprising 18.26% and 12.42%, respectively, of resident clinical experience. Within the category of circulatory disorders, congestive heart failure and acute myocardial infarction were the most common conditions seen; for respiratory disorders, pneumonia, chronic airway obstruction, and asthma were most commonly encountered. In aggregate, symptoms and ill‐defined conditions, and disorders of the circulatory and respiratory systems accounted for 50% of resident clinical experience.

Individual resident clinical experience varied as well. As shown in Table 4, for a group of 6 PGY‐1s, there was substantial variability in the ICD‐9 diagnostic categories. For example, the percentages of codes falling into the cardiovascular disease category ranged from 15.27% to 27.91%, and for respiratory disease ranged from 8.22% to 18.55%. These data suggest that there may be sizable differences in the proportions of various clinical conditions seen by residents over a year of training.

DISCUSSION

Years ago, residency training transitioned from a predominantly bedside experience to a curriculum with a large didactic, non‐bedside component, following parameters defined by organizations such as the Accreditation Council for Graduate Medical Education. Residency training is undergoing substantial change to become competency‐based and to organize learning around patient care experiences.2, 3, 9 The Educational Innovation Project of the Residency Review Committee for Internal Medicine is one such endeavor to help develop new methods by which to accomplish this.1 Effective incorporation of innovative experiential learning methods, based on the core competencies, will require a detailed knowledge of resident clinical experience during the course of their training, yet such data have been sparse in internal medicine. Sequist et al. analyzed data from an electronic medical record to assess resident clinical experience in the outpatient setting.4 Bachur and Nagler have used an electronic patient tracking system to assess the clinical experience of pediatric emergency medicine fellows.5, 6 Most attempts to describe resident clinical experience have relied upon extracting diagnostic information from medical records, case logs, etc, though in another approach, Rohrbaugh et al. reviewed psychiatric resident prescription profiles,7 which might provide some indirect data on clinical experience if applied to internal medicine.

In this study, we attempted to quantify resident clinical experience using resident‐selected ICD‐9 codes, in contrast to other methods that have relied upon medical record review and other resident‐independent approaches. There are various strengths and limitations to this approach. Using the ICD‐9 system provides a number of strengths, a major one being standardization, allowing comparisons between different programs and perhaps even facilitating the development of guidelines for resident clinical experience. In addition, this approach using the ICD‐9 system could be readily implemented at any institution and does not require any specific technology. While we chose to do this through our handoff system, an institution could use any of a variety of other systems to accomplish this. For example, resident‐entered ICD‐9 coding systems could be incorporated into electronic discharge summaries, history and physicals, or progress notes. There may also be some practical benefits to having residents learn how to use the ICD‐9 system at this stage of their careers.

There are limitations to this approach as well. The ICD‐9 system was not intended to be used for medical education purposes. There are features of it that can make finding the best diagnosis difficult, and routes to it may at times seem counterintuitive. While we did not carry out resident surveys, a number of residents anecdotally mentioned that it took time to become comfortable using the system, and it could be challenging at times to find a diagnosis description that best fit what they were looking for. To make diagnosis selection easier, we created an organ system‐based dropdown list in the handoff tool so that when residents select an organ system, another list opens up containing commonly used ICD‐9 codes. This grouping is based on organ system alone and does not necessarily follow the ICD‐9 grouping (in contrast, our reported data in this article are all based on ICD‐9 grouping). A search tool to allow searching the entire ICD‐9 database was also made available on the handoff tool. Other factors that could limit diagnosis code accuracy could be lack of clinical knowledge, and error as a result of pressure to come up with a diagnosis because of the hard stop design of our system, in which residents were required to enter a primary diagnosis, potentially causing alert fatigue. A validation assessment that we carried out revealed fairly good agreement with the specific ICD‐9 codes chosen by the resident, but greater accuracy would be desirable. Further education on diagnosis selection and refinements to the handoff tool should help facilitate this. We are currently addressing this by ongoing education on diagnosis selection and by having the hospitalists share the handoff tool with the residents, allowing them to provide direct feedback on diagnostic selections.

More than 19% of the diagnoses selected by residents fell into the category of symptoms and ill‐defined conditions. This raises a number of potential educational issues. One of those is that if residents do, in fact, encounter such entities at such a high frequency, then the internal medicine curriculum must be structured in such a way as to complement this clinical experience with a comprehensive learning program. However, we must also consider the possibility that, in many such instances, a more definitive diagnosis became evident by the time of discharge and this may not have been reflected in the ICD‐9 code that the resident chose. Hence, the category of symptoms and ill‐defined conditions may actually be somewhat smaller than our findings would suggest.

Many issues will need to be addressed as programs obtain more data on their residents' clinical experience. While there may be many reasons to use the ICD‐9 system for selecting diagnoses including those listed above, the system by which ICD‐9 groups diagnoses might not provide ideal educational information, again as the ICD‐9 system was not designed for this purpose. While in this article we have reported the residents' diagnostic encounters grouped according to the ICD‐9 grouping system to provide an initial standardized description, grouping according to another diagnostic system that is felt to be more educationally meaningful may be preferred.

While one might assume that a higher frequency of exposure to certain clinical conditions should enhance competency, that relationship may not be straightforward in internal medicine. For surgical procedures, there are, in fact, data to show improved outcomes for surgeons with higher operative volumes for those procedures,10 but in internal medicine, we do not have data to demonstrate that competence of a resident caring for a particular condition is enhanced by experience alone. Therefore, as programs obtain more data on clinical experience, it will be important that the focus be kept on quality as opposed to quantity.

Obtaining data on resident clinical experience might greatly facilitate experiential learning approaches. For example, as residents go through training and encounter specific diagnostic conditions, those experiences could be supplemented by various learning innovations to make those experiences more meaningful and, hopefully, more likely to result in the development of competence, though that will require measurement. In our program, for example, we have incorporated an approach using illness scenarios, in that when residents have had a certain level of clinical experience with a given clinical condition, they are assembled in small groups and competency‐based case discussions are carried out with a preceptor. In addition, for those instances in which an individual resident may lack direct clinical experience in a certain area, this might be addressed by interventions to increase their contact with those conditions and/or targeted learning interventions to help develop competence. A resident found to be lacking in clinical experience in a certain area could be assigned to the care of more patients with that condition, or to spending more time in a venue in which that condition is more likely to be encountered. Various learning activities including didactics, case discussions, simulation, self‐directed learning, and others could also be used to compensate for such variability. Furthermore, if a residency program's aggregate clinical experience is divergent from some desirable standard yet to be determined, a detailed knowledge of this could help guide that program's curriculum revision. For example, for residents in a program in which there is relatively low exposure to patients with oncological issues, this could be compensated for by external rotations to achieve more clinical experience in oncology, as well as supplementation of the curriculum with additional learning activities in oncology, which could include small group discussions, self‐directed learning activities, case discussions, and others. While at present there are no defined standards for clinical experience and it remains to be seen if there would be a correlation with development of competence, no such standard would serve a purpose if programs did not have reliable and practical means of clinical experience assessment.

In summary, resident‐selected ICD‐9 codes may be a useful means to obtain data regarding resident clinical experience in internal medicine. Such data may be useful to residency training programs in developing new curricula based on experiential learning.

Internal medicine residency training continues to evolve as competency‐based and with education organized around patient care.13 Making the patient the center of resident education provides an opportunity for experiential learning in which learning can be organized around the clinical conditions that residents encounter. Despite the renewed emphasis on using patient experience as the basis for residency education, little is known regarding what specific diagnostic conditions are seen by internal medicine residents throughout their training. Attempts have been made to quantify resident clinical experience in various fields, using approaches such as review of medical records, case logs, and prescription profiles, but to date, we lack systematic methods to obtain clinical experience data for internal medicine residents.47

While residency curricula in internal medicine typically outlines specific rotations in various clinical areas such as general medical wards, cardiology services, and intensive care units, time spent on such rotations does not necessarily provide quantitative data on the actual clinical conditions that residents encounter, nor does it ensure consistent clinical experience between residents. It is plausible that there may be substantial variability in clinical experience between residents within the same program, and that the overall spectrum of clinical disorders seen by residents in a program may or may not be consistent with a desired optimum, though this is yet to be defined.

If residency education in internal medicine is to progressively incorporate more experiential learning, detailed knowledge of the clinical conditions seen by residents should be useful, not only for overall curriculum design, but this might also allow for various educational interventions to be made when there are variations in clinical experience between residents. Our program has been interested in the application of electronic resources for the improvement of patient care, such as through the handoff process and the use of personal digital assistants.8 We previously did a small analysis of clinical conditions seen by residents through non‐International Classification of Diseases, Ninth Revision (ICD‐9)‐based data they entered onto personal digital assistants. This suggested to us that electronic resources used by residents might serve as a venue by which they could enter diagnostic information which we could use to generate a more detailed analysis of the clinical conditions that they see. Here we describe a method by which we have attempted to quantify resident clinical experience in internal medicine using a modification of an electronic handoff system.

METHODS

The study was conducted within the Internal Medicine Residency Program at the Long Island Jewish Medical Center in New Hyde Park, New York, part of the North ShoreLong Island Jewish Health System, and was approved by the Institutional Review Board. This work was carried out as part of our participation in the Educational Innovation Project of the Residency Review Committee for Internal Medicine. A central objective of our proposal was to develop a method to assess residents' clinical experience on an individual and an aggregate basis. A group of faculty and residents in our residency program developed an electronic handoff tool which residents use for rapid access to key clinical data for their patients and for the handoff of clinical information for on call coverage. This handoff tool was developed with the technical assistance of MedTech Notes LLC which owns Patient Data Transfer System (PDTS) HandOff Note. We modified the handoff tool to include a section in which residents were required to enter a primary diagnosis for each of their patients (a hard stop design). We chose to use the ICD‐9 system for standardization and created two methods to select the code: 1) an organ system‐based dropdown list containing frequently used codes and 2) a search box allowing for searching of the complete ICD‐9 database. For the organ‐based dropdown list, selection of that organ system would reveal a brief list of frequently used codes to make it easier for residents to find them. Prior to using the handoff tool with the ICD‐9based primary diagnosis coding system, training sessions with the residents were conducted by 3 of the investigators along with 3 chief medical residents. These sessions included training not only in technical aspects of how to find diagnosis codes, but also how to make decisions regarding what the primary diagnosis should be. We also instructed our postgraduate year (PGY)‐1s to update their diagnostic selections during the course of the hospital stay.

Each data point represents a resident caring for a patient with a specific diagnostic entity, and is counted once for that resident's period of taking care of that patient. Thirty‐three PGY‐1s were studied and, on the internal medicine service, they were supervised by either hospitalist faculty or voluntary faculty in comparable proportions. If the patient's care is taken over by another resident, that second resident was also recorded as having had a diagnostic encounter with that patient, hence 1 patient could provide experience with the same diagnostic entity for 1 or more residents. Using this method, the denominator is not patients seen, but residentpatient diagnostic encounters that have taken place. The ICD‐9 diagnostic conditions entered by the residents were grouped using the ICD‐9 system. Individual diagnostic profiles for each resident, as well as an aggregate profile for all residents to reflect the residency program as a whole, were generated. We also carried out an analysis of the ICD‐9 codes entered by 6 consecutive PGY‐1s to assess how the diagnostic spectrum might vary among a small sampling of PGY‐1s. In order to evaluate the accuracy of the residents' diagnostic selections, we carried out a validation assessment using a tool used by the residents' supervising hospitalists (who were the attendings of record for those patients). This was carried out on a subset of patients and could be done at any time during the hospital stay. The hospitalists were asked to review their residents' ICD‐9 codes and indicate whether they agreed or disagreed.

RESULTS

A total of 7562 residentpatient diagnostic encounters were studied from July 1, 2007 through June 1, 2008. Mean patient age was 66 19.4 years. The age distribution is given in Table 1 and reveals that 65% of diagnostic encounters were with patients age 60 years or greater. Twelve housestaff teams were studied, each consisting of 2 PGY‐1s and a supervising PGY‐2 or PGY‐3 resident. All ICD‐9 codes were selected by categorical and preliminary internal medicine PGY‐1s on medical ward and intensive care unit rotations. Residents from other departments doing rotations on the medical service were excluded. A validation assessment of 341 patients indicated 83.3% agreement by the supervising hospitalist with the primary ICD‐9 code selected. ICD‐9 codes were then grouped and categorized using ICD‐9 nomenclature with the distribution provided in Table 2. A wide spectrum of clinical conditions is apparent including symptoms and ill‐defined conditions, circulatory disorders, respiratory disorders, neoplasms, genitourinary disorders, digestive disorders, diseases of the blood/blood forming organs, endocrinologic/nutritional/metabolic/emmmune disorders, and disorders of the skin and subcutaneous tissue, overall accounting for about 86% of resident clinical experience.

We also examined the most common diagnostic conditions within each of these categories. The 3 most common ICD‐9 codes entered by residents within each category are provided in Table 3. Symptoms and ill‐defined conditions represent a sizable portion of resident clinical experience (19.51%). Within this category, the most common conditions were fever; abdominal pain (unspecified site); and chest pain, unspecified. Disorders of the circulatory and respiratory systems were the next most common categories of conditions seen by residents, comprising 18.26% and 12.42%, respectively, of resident clinical experience. Within the category of circulatory disorders, congestive heart failure and acute myocardial infarction were the most common conditions seen; for respiratory disorders, pneumonia, chronic airway obstruction, and asthma were most commonly encountered. In aggregate, symptoms and ill‐defined conditions, and disorders of the circulatory and respiratory systems accounted for 50% of resident clinical experience.

Individual resident clinical experience varied as well. As shown in Table 4, for a group of 6 PGY‐1s, there was substantial variability in the ICD‐9 diagnostic categories. For example, the percentages of codes falling into the cardiovascular disease category ranged from 15.27% to 27.91%, and for respiratory disease ranged from 8.22% to 18.55%. These data suggest that there may be sizable differences in the proportions of various clinical conditions seen by residents over a year of training.

DISCUSSION

Years ago, residency training transitioned from a predominantly bedside experience to a curriculum with a large didactic, non‐bedside component, following parameters defined by organizations such as the Accreditation Council for Graduate Medical Education. Residency training is undergoing substantial change to become competency‐based and to organize learning around patient care experiences.2, 3, 9 The Educational Innovation Project of the Residency Review Committee for Internal Medicine is one such endeavor to help develop new methods by which to accomplish this.1 Effective incorporation of innovative experiential learning methods, based on the core competencies, will require a detailed knowledge of resident clinical experience during the course of their training, yet such data have been sparse in internal medicine. Sequist et al. analyzed data from an electronic medical record to assess resident clinical experience in the outpatient setting.4 Bachur and Nagler have used an electronic patient tracking system to assess the clinical experience of pediatric emergency medicine fellows.5, 6 Most attempts to describe resident clinical experience have relied upon extracting diagnostic information from medical records, case logs, etc, though in another approach, Rohrbaugh et al. reviewed psychiatric resident prescription profiles,7 which might provide some indirect data on clinical experience if applied to internal medicine.

In this study, we attempted to quantify resident clinical experience using resident‐selected ICD‐9 codes, in contrast to other methods that have relied upon medical record review and other resident‐independent approaches. There are various strengths and limitations to this approach. Using the ICD‐9 system provides a number of strengths, a major one being standardization, allowing comparisons between different programs and perhaps even facilitating the development of guidelines for resident clinical experience. In addition, this approach using the ICD‐9 system could be readily implemented at any institution and does not require any specific technology. While we chose to do this through our handoff system, an institution could use any of a variety of other systems to accomplish this. For example, resident‐entered ICD‐9 coding systems could be incorporated into electronic discharge summaries, history and physicals, or progress notes. There may also be some practical benefits to having residents learn how to use the ICD‐9 system at this stage of their careers.

There are limitations to this approach as well. The ICD‐9 system was not intended to be used for medical education purposes. There are features of it that can make finding the best diagnosis difficult, and routes to it may at times seem counterintuitive. While we did not carry out resident surveys, a number of residents anecdotally mentioned that it took time to become comfortable using the system, and it could be challenging at times to find a diagnosis description that best fit what they were looking for. To make diagnosis selection easier, we created an organ system‐based dropdown list in the handoff tool so that when residents select an organ system, another list opens up containing commonly used ICD‐9 codes. This grouping is based on organ system alone and does not necessarily follow the ICD‐9 grouping (in contrast, our reported data in this article are all based on ICD‐9 grouping). A search tool to allow searching the entire ICD‐9 database was also made available on the handoff tool. Other factors that could limit diagnosis code accuracy could be lack of clinical knowledge, and error as a result of pressure to come up with a diagnosis because of the hard stop design of our system, in which residents were required to enter a primary diagnosis, potentially causing alert fatigue. A validation assessment that we carried out revealed fairly good agreement with the specific ICD‐9 codes chosen by the resident, but greater accuracy would be desirable. Further education on diagnosis selection and refinements to the handoff tool should help facilitate this. We are currently addressing this by ongoing education on diagnosis selection and by having the hospitalists share the handoff tool with the residents, allowing them to provide direct feedback on diagnostic selections.

More than 19% of the diagnoses selected by residents fell into the category of symptoms and ill‐defined conditions. This raises a number of potential educational issues. One of those is that if residents do, in fact, encounter such entities at such a high frequency, then the internal medicine curriculum must be structured in such a way as to complement this clinical experience with a comprehensive learning program. However, we must also consider the possibility that, in many such instances, a more definitive diagnosis became evident by the time of discharge and this may not have been reflected in the ICD‐9 code that the resident chose. Hence, the category of symptoms and ill‐defined conditions may actually be somewhat smaller than our findings would suggest.

Many issues will need to be addressed as programs obtain more data on their residents' clinical experience. While there may be many reasons to use the ICD‐9 system for selecting diagnoses including those listed above, the system by which ICD‐9 groups diagnoses might not provide ideal educational information, again as the ICD‐9 system was not designed for this purpose. While in this article we have reported the residents' diagnostic encounters grouped according to the ICD‐9 grouping system to provide an initial standardized description, grouping according to another diagnostic system that is felt to be more educationally meaningful may be preferred.

While one might assume that a higher frequency of exposure to certain clinical conditions should enhance competency, that relationship may not be straightforward in internal medicine. For surgical procedures, there are, in fact, data to show improved outcomes for surgeons with higher operative volumes for those procedures,10 but in internal medicine, we do not have data to demonstrate that competence of a resident caring for a particular condition is enhanced by experience alone. Therefore, as programs obtain more data on clinical experience, it will be important that the focus be kept on quality as opposed to quantity.

Obtaining data on resident clinical experience might greatly facilitate experiential learning approaches. For example, as residents go through training and encounter specific diagnostic conditions, those experiences could be supplemented by various learning innovations to make those experiences more meaningful and, hopefully, more likely to result in the development of competence, though that will require measurement. In our program, for example, we have incorporated an approach using illness scenarios, in that when residents have had a certain level of clinical experience with a given clinical condition, they are assembled in small groups and competency‐based case discussions are carried out with a preceptor. In addition, for those instances in which an individual resident may lack direct clinical experience in a certain area, this might be addressed by interventions to increase their contact with those conditions and/or targeted learning interventions to help develop competence. A resident found to be lacking in clinical experience in a certain area could be assigned to the care of more patients with that condition, or to spending more time in a venue in which that condition is more likely to be encountered. Various learning activities including didactics, case discussions, simulation, self‐directed learning, and others could also be used to compensate for such variability. Furthermore, if a residency program's aggregate clinical experience is divergent from some desirable standard yet to be determined, a detailed knowledge of this could help guide that program's curriculum revision. For example, for residents in a program in which there is relatively low exposure to patients with oncological issues, this could be compensated for by external rotations to achieve more clinical experience in oncology, as well as supplementation of the curriculum with additional learning activities in oncology, which could include small group discussions, self‐directed learning activities, case discussions, and others. While at present there are no defined standards for clinical experience and it remains to be seen if there would be a correlation with development of competence, no such standard would serve a purpose if programs did not have reliable and practical means of clinical experience assessment.

In summary, resident‐selected ICD‐9 codes may be a useful means to obtain data regarding resident clinical experience in internal medicine. Such data may be useful to residency training programs in developing new curricula based on experiential learning.

Severe sepsis and septic shock are associated with excess mortality when inappropriate initial antimicrobial therapy, defined as an antimicrobial regimen that lacks in vitro activity against the isolated organism(s) responsible for the infection, is administered.14 Unfortunately, bacterial resistance to antibiotics is increasing and creates a therapeutic challenge for clinicians when treating patients with serious infections, such as severe sepsis. Increasing rates of bacterial resistance leads many clinicians to empirically treat critically ill patients with broad‐spectrum antibiotics, which can perpetuate the cycle of increasing resistance.5, 6 Conversely, inappropriate initial antimicrobial therapy can lead to treatment failures and adverse patient outcomes.7 Individuals with severe sepsis appear to be at particularly high risk of excess mortality when inappropriate initial antimicrobial therapy is administered.8, 9

The most recent Surviving Sepsis Guidelines recommend empiric combination therapy targeting Gram‐negative bacteria, particularly for patients with known or suspected Pseudomonas infections, as a means to decrease the likelihood of administering inappropriate initial antimicrobial therapy.10 However, the selection of an antimicrobial regimen that is active against the causative pathogen(s) is problematic, as the treating physician usually does not know the susceptibilities of the pathogen(s) for the selected empiric antibiotics. Therefore, we performed a study with the main goal of determining whether resistance to the initially prescribed antimicrobial regimen was associated with clinical outcome in patients with severe sepsis attributed to Gram‐negative bacteremia.

Materials and Methods

Results

Antimicrobial Treatment and Resistance

Among the study patients, 358 (66.9%) received cefepime, 102 (19.1%) received piperacillin‐tazobactam, and 75 (14.0%) received a carbapenem (meropenem or imipenem) as their initial antibiotic treatment. There were 169 (31.6%) patients who received initial combination therapy with either an aminoglycoside (n = 99, 58.6%) or ciprofloxacin (n = 70, 41.4%). Eighty‐two (15.3%) patients were infected with a pathogen that was resistant to the initial antibiotic treatment regimen [cefepime (n = 41; 50.0%), piperacillin‐tazobactam (n = 25; 30.5%), or imipenem/meropenem (n = 16; 19.5%), plus either an aminoglycoside or ciprofloxacin (n = 28; 34.1%)], and were classified as receiving inappropriate initial antibiotic therapy. Among the 453 (84.7%) patients infected with a pathogen that was susceptible to the initial antibiotic regimen, there was no relationship identified between minimum inhibitory concentration values and hospital mortality.

Patients infected with a pathogen resistant to the initial antibiotic regimen had significantly greater risk of hospital mortality (63.4% vs 40.0%; P < 0.001) (Figure 1). For the 82 individuals infected with a pathogen that was resistant to the initial antibiotic regimen, no difference in hospital mortality was observed among those prescribed initial combination treatment with an aminoglycoside (n = 17) (64.7% vs 61.1%; P = 0.790) or ciprofloxacin (n = 11) (72.7% vs 61.1%; P = 0.733) compared to monotherapy (n = 54). Similarly, among the patients infected with a pathogen that was susceptible to the initial antibiotic regimen, there was no difference in hospital mortality among those whose bloodstream isolate was only susceptible to the prescribed aminoglycoside (n = 12) compared to patients with isolates that were susceptible to the prescribed beta‐lactam antibiotic (n = 441) (41.7% vs 39.9%; P = 0.902).

Figure 1

Logistic regression analysis identified infection with a pathogen resistant to the initial antibiotic regimen [adjusted odds ratio (AOR), 2.28; 95% confidence interval (CI), 1.69‐3.08; P = 0.006], increasing APACHE II scores (1‐point increments) (AOR, 1.13; 95% CI, 1.10‐1.15; P < 0.001), the need for vasopressors (AOR, 2.57; 95% CI, 2.15‐3.53; P < 0.001), the need for mechanical ventilation (AOR, 2.54; 95% CI, 2.19‐3.47; P < 0.001), healthcare‐associated hospital‐onset infection (AOR, 1.67; 95% CI, 1.32‐2.10; P =0.027), and infection with Pseudomonas aeruginosa (AOR, 2.21; 95% CI, 1.74‐2.86; P =0.002) as independent risk factors for hospital mortality (Hosmer‐Lemeshow goodness‐of‐fit test = 0.305). The model explained between 29.7% (Cox and Snell R square) and 39.8% (Nagelkerke R squared) of the variance in hospital mortality, and correctly classified 75.3% of cases.

Discussion

Our study demonstrated that hospital nonsurvivors with severe sepsis attributed to Gram‐negative bacteremia had significantly greater rates of resistance to their initially prescribed antibiotic regimen compared to hospital survivors. This observation was confirmed in a multivariate analysis controlling for severity of illness and other potential confounding variables. Additionally, acquired organ system derangements and hospital length of stay were greater for patients infected with Gram‐negative pathogens resistant to the empiric antibiotic regimen. We also observed no survival advantage with the use of combination antimicrobial therapy for the subgroup of patients whose pathogens were resistant to the initially prescribed antibiotic regimen. Lastly, no difference in mortality was observed for patients with bacterial isolates that were susceptible only to the prescribed aminoglycoside compared to those with isolates susceptible to the prescribed beta‐lactam antibiotic.

Several previous investigators have linked antibiotic resistance and outcome in patients with serious infections attributed to Gram‐negative bacteria. Tam et al. examined 34 patients with Pseudomonas aeruginosa bacteremia having elevated MICs to piperacillin‐tazobactam (32 g/mL) that were reported as susceptible.19 In seven of these cases, piperacillin‐tazobactam was prescribed empirically, whereas other agents directed against Gram‐negative bacteria were employed in the other patients (carbapenems, aminoglycosides). Thirty‐day mortality was significantly greater for the patients treated with piperacillin‐tazobactam (85.7% vs 22.2%; P = 0.004), and a multivariate analysis found treatment with piperacillin‐tazobactam to be independently associated with 30‐day mortality. Similarly, Bhat et al. examined 204 episodes of bacteremia caused by Gram‐negative bacteria for which patients received cefepime.20 Patients infected with a Gram‐negative bacteria having an MIC to cefepime greater than, or equal to, 8 g/mL had a significantly greater 28‐day mortality compared to patients infected with isolates having an MIC to cefepime that was less than 8 g/mL (54.8% vs 24.1%; P = 0.001).

Our findings are consistent with earlier studies of patients with serious Gram‐negative infections including bacteremia and nosocomial pneumonia. Micek et al. showed that patients with Pseudomonas aeruginosa bacteremia who received inappropriate initial antimicrobial therapy had a greater risk of hospital mortality compared to patients initially treated with an antimicrobial regimen having activity for the Pseudomonas isolate based on in vitro susceptibility testing.21 Similarly, Trouillet et al.,22 Beardsley et al.,23 and Heyland et al.24 found that combination antimicrobial regimens directed against Gram‐negative bacteria in patients with nosocomial pneumonia were more likely to be appropriate based on the antimicrobial susceptibility patterns of the organisms compared to monotherapy. In a more recent study, Micek et al. demonstrated that combination antimicrobial therapy directed against severe sepsis attributed to Gram‐negative bacteria was associated with improved outcomes compared to monotherapy, especially when the combination agent was an aminoglycoside.25 However, empiric combination therapy that included an aminoglycoside was also associated with increased nephrotoxicity which makes the empiric use of aminoglycosides in all patients with suspected Gram‐negative severe sepsis problematic.25, 26 Nevertheless, the use of combination therapy represents a potential strategy to maximize the administration of appropriate treatment for serious Gram‐negative bacterial infections.

Rapid assessment of antimicrobial susceptibility is another strategy that offers the possibility of identifying the resistance pattern of Gram‐negative pathogens quickly in order to provide more appropriate treatment. Bouza et al. found that use of a rapid E‐test on the respiratory specimens of patients with ventilator‐associated pneumonia was associated with fewer days of fever, fewer days of antibiotic administration until resolution of the episode of ventilator‐associated pneumonia, decreased antibiotic consumption, less Clostridium difficile‐associated diarrhea, lower costs of antimicrobial agents, and fewer days receiving mechanical ventilation.27 Other methods for the rapid identification of resistant bacteria include real‐time polymerase chain reaction assays based on hybridization probes to identify specific resistance mechanisms in bacteria.28 Application of such methods for identification of broad categories of resistance mechanisms in Gram‐negative bacteria offer the possibility of tailoring initial antimicrobial regimens in order to provide appropriate therapy in a more timely manner.

Our study has several important limitations that should be noted. First, the study was performed at a single center and the results may not be generalizable to other institutions. However, the findings from other investigators corroborate the importance of antimicrobial resistance as a predictor of outcome for patients with serious Gram‐negative infections.19, 20 Additionally, a similar association has been observed in patients with methicillin‐resistant Staphylococcus aureus bacteremia, supporting the more general importance of antimicrobial resistance as an outcome predictor.29 Second, the method employed for determining MICs was a literature‐based linear regression method correlating disk diffusion diameters with broth dilution MIC determinations. Therefore, the lack of correlation we observed between MIC values and outcome for susceptible Gram‐negative isolates associated with severe sepsis requires further confirmation. Third, we only examined 3 antibiotics, or antibiotic classes, so our results may not be applicable to other agents. This also applies to doripenem, as we did not have that specific carbapenem available at the time this investigation took place.

Another important limitation of our study is the relatively small number of individuals infected with a pathogen that was resistant to the initial treatment regimen, or only susceptible to the aminoglycoside when combination therapy was prescribed. This limited our ability to detect meaningful associations in these subgroups of patients, to include whether or not combination therapy influenced their clinical outcome. Finally, we did not examine the exact timing of antibiotic therapy relative to the onset of severe sepsis. Instead we used a 12‐hour window from when subsequently positive blood cultures were drawn to the administration of initial antibiotic therapy. Other investigators have shown that delays in initial appropriate therapy of more than one hour for patients with septic shock increases the risk of death.9, 30 Failure to include the exact timing of therapy could have resulted in a final multivariate model that includes prediction variables that would not otherwise have been incorporated.

In summary, we demonstrated that resistance to the initial antibiotic treatment regimen was associated with a greater risk of hospital mortality in patients with severe sepsis attributed to Gram‐negative bacteremia. These findings imply that more rapid assessment of antimicrobial susceptibility could result in improved prescription of antibiotics in order to maximize initial administration of appropriate therapy. Future studies are required to address whether rapid determination of antimicrobial susceptibility can result in more effective administration of appropriate therapy, and if this can result in improved patient outcomes.

Severe sepsis and septic shock are associated with excess mortality when inappropriate initial antimicrobial therapy, defined as an antimicrobial regimen that lacks in vitro activity against the isolated organism(s) responsible for the infection, is administered.14 Unfortunately, bacterial resistance to antibiotics is increasing and creates a therapeutic challenge for clinicians when treating patients with serious infections, such as severe sepsis. Increasing rates of bacterial resistance leads many clinicians to empirically treat critically ill patients with broad‐spectrum antibiotics, which can perpetuate the cycle of increasing resistance.5, 6 Conversely, inappropriate initial antimicrobial therapy can lead to treatment failures and adverse patient outcomes.7 Individuals with severe sepsis appear to be at particularly high risk of excess mortality when inappropriate initial antimicrobial therapy is administered.8, 9

The most recent Surviving Sepsis Guidelines recommend empiric combination therapy targeting Gram‐negative bacteria, particularly for patients with known or suspected Pseudomonas infections, as a means to decrease the likelihood of administering inappropriate initial antimicrobial therapy.10 However, the selection of an antimicrobial regimen that is active against the causative pathogen(s) is problematic, as the treating physician usually does not know the susceptibilities of the pathogen(s) for the selected empiric antibiotics. Therefore, we performed a study with the main goal of determining whether resistance to the initially prescribed antimicrobial regimen was associated with clinical outcome in patients with severe sepsis attributed to Gram‐negative bacteremia.

Materials and Methods

Results

Antimicrobial Treatment and Resistance

Among the study patients, 358 (66.9%) received cefepime, 102 (19.1%) received piperacillin‐tazobactam, and 75 (14.0%) received a carbapenem (meropenem or imipenem) as their initial antibiotic treatment. There were 169 (31.6%) patients who received initial combination therapy with either an aminoglycoside (n = 99, 58.6%) or ciprofloxacin (n = 70, 41.4%). Eighty‐two (15.3%) patients were infected with a pathogen that was resistant to the initial antibiotic treatment regimen [cefepime (n = 41; 50.0%), piperacillin‐tazobactam (n = 25; 30.5%), or imipenem/meropenem (n = 16; 19.5%), plus either an aminoglycoside or ciprofloxacin (n = 28; 34.1%)], and were classified as receiving inappropriate initial antibiotic therapy. Among the 453 (84.7%) patients infected with a pathogen that was susceptible to the initial antibiotic regimen, there was no relationship identified between minimum inhibitory concentration values and hospital mortality.

Patients infected with a pathogen resistant to the initial antibiotic regimen had significantly greater risk of hospital mortality (63.4% vs 40.0%; P < 0.001) (Figure 1). For the 82 individuals infected with a pathogen that was resistant to the initial antibiotic regimen, no difference in hospital mortality was observed among those prescribed initial combination treatment with an aminoglycoside (n = 17) (64.7% vs 61.1%; P = 0.790) or ciprofloxacin (n = 11) (72.7% vs 61.1%; P = 0.733) compared to monotherapy (n = 54). Similarly, among the patients infected with a pathogen that was susceptible to the initial antibiotic regimen, there was no difference in hospital mortality among those whose bloodstream isolate was only susceptible to the prescribed aminoglycoside (n = 12) compared to patients with isolates that were susceptible to the prescribed beta‐lactam antibiotic (n = 441) (41.7% vs 39.9%; P = 0.902).

Figure 1

Logistic regression analysis identified infection with a pathogen resistant to the initial antibiotic regimen [adjusted odds ratio (AOR), 2.28; 95% confidence interval (CI), 1.69‐3.08; P = 0.006], increasing APACHE II scores (1‐point increments) (AOR, 1.13; 95% CI, 1.10‐1.15; P < 0.001), the need for vasopressors (AOR, 2.57; 95% CI, 2.15‐3.53; P < 0.001), the need for mechanical ventilation (AOR, 2.54; 95% CI, 2.19‐3.47; P < 0.001), healthcare‐associated hospital‐onset infection (AOR, 1.67; 95% CI, 1.32‐2.10; P =0.027), and infection with Pseudomonas aeruginosa (AOR, 2.21; 95% CI, 1.74‐2.86; P =0.002) as independent risk factors for hospital mortality (Hosmer‐Lemeshow goodness‐of‐fit test = 0.305). The model explained between 29.7% (Cox and Snell R square) and 39.8% (Nagelkerke R squared) of the variance in hospital mortality, and correctly classified 75.3% of cases.

Discussion

Our study demonstrated that hospital nonsurvivors with severe sepsis attributed to Gram‐negative bacteremia had significantly greater rates of resistance to their initially prescribed antibiotic regimen compared to hospital survivors. This observation was confirmed in a multivariate analysis controlling for severity of illness and other potential confounding variables. Additionally, acquired organ system derangements and hospital length of stay were greater for patients infected with Gram‐negative pathogens resistant to the empiric antibiotic regimen. We also observed no survival advantage with the use of combination antimicrobial therapy for the subgroup of patients whose pathogens were resistant to the initially prescribed antibiotic regimen. Lastly, no difference in mortality was observed for patients with bacterial isolates that were susceptible only to the prescribed aminoglycoside compared to those with isolates susceptible to the prescribed beta‐lactam antibiotic.

Several previous investigators have linked antibiotic resistance and outcome in patients with serious infections attributed to Gram‐negative bacteria. Tam et al. examined 34 patients with Pseudomonas aeruginosa bacteremia having elevated MICs to piperacillin‐tazobactam (32 g/mL) that were reported as susceptible.19 In seven of these cases, piperacillin‐tazobactam was prescribed empirically, whereas other agents directed against Gram‐negative bacteria were employed in the other patients (carbapenems, aminoglycosides). Thirty‐day mortality was significantly greater for the patients treated with piperacillin‐tazobactam (85.7% vs 22.2%; P = 0.004), and a multivariate analysis found treatment with piperacillin‐tazobactam to be independently associated with 30‐day mortality. Similarly, Bhat et al. examined 204 episodes of bacteremia caused by Gram‐negative bacteria for which patients received cefepime.20 Patients infected with a Gram‐negative bacteria having an MIC to cefepime greater than, or equal to, 8 g/mL had a significantly greater 28‐day mortality compared to patients infected with isolates having an MIC to cefepime that was less than 8 g/mL (54.8% vs 24.1%; P = 0.001).

Our findings are consistent with earlier studies of patients with serious Gram‐negative infections including bacteremia and nosocomial pneumonia. Micek et al. showed that patients with Pseudomonas aeruginosa bacteremia who received inappropriate initial antimicrobial therapy had a greater risk of hospital mortality compared to patients initially treated with an antimicrobial regimen having activity for the Pseudomonas isolate based on in vitro susceptibility testing.21 Similarly, Trouillet et al.,22 Beardsley et al.,23 and Heyland et al.24 found that combination antimicrobial regimens directed against Gram‐negative bacteria in patients with nosocomial pneumonia were more likely to be appropriate based on the antimicrobial susceptibility patterns of the organisms compared to monotherapy. In a more recent study, Micek et al. demonstrated that combination antimicrobial therapy directed against severe sepsis attributed to Gram‐negative bacteria was associated with improved outcomes compared to monotherapy, especially when the combination agent was an aminoglycoside.25 However, empiric combination therapy that included an aminoglycoside was also associated with increased nephrotoxicity which makes the empiric use of aminoglycosides in all patients with suspected Gram‐negative severe sepsis problematic.25, 26 Nevertheless, the use of combination therapy represents a potential strategy to maximize the administration of appropriate treatment for serious Gram‐negative bacterial infections.

Rapid assessment of antimicrobial susceptibility is another strategy that offers the possibility of identifying the resistance pattern of Gram‐negative pathogens quickly in order to provide more appropriate treatment. Bouza et al. found that use of a rapid E‐test on the respiratory specimens of patients with ventilator‐associated pneumonia was associated with fewer days of fever, fewer days of antibiotic administration until resolution of the episode of ventilator‐associated pneumonia, decreased antibiotic consumption, less Clostridium difficile‐associated diarrhea, lower costs of antimicrobial agents, and fewer days receiving mechanical ventilation.27 Other methods for the rapid identification of resistant bacteria include real‐time polymerase chain reaction assays based on hybridization probes to identify specific resistance mechanisms in bacteria.28 Application of such methods for identification of broad categories of resistance mechanisms in Gram‐negative bacteria offer the possibility of tailoring initial antimicrobial regimens in order to provide appropriate therapy in a more timely manner.

Our study has several important limitations that should be noted. First, the study was performed at a single center and the results may not be generalizable to other institutions. However, the findings from other investigators corroborate the importance of antimicrobial resistance as a predictor of outcome for patients with serious Gram‐negative infections.19, 20 Additionally, a similar association has been observed in patients with methicillin‐resistant Staphylococcus aureus bacteremia, supporting the more general importance of antimicrobial resistance as an outcome predictor.29 Second, the method employed for determining MICs was a literature‐based linear regression method correlating disk diffusion diameters with broth dilution MIC determinations. Therefore, the lack of correlation we observed between MIC values and outcome for susceptible Gram‐negative isolates associated with severe sepsis requires further confirmation. Third, we only examined 3 antibiotics, or antibiotic classes, so our results may not be applicable to other agents. This also applies to doripenem, as we did not have that specific carbapenem available at the time this investigation took place.

Another important limitation of our study is the relatively small number of individuals infected with a pathogen that was resistant to the initial treatment regimen, or only susceptible to the aminoglycoside when combination therapy was prescribed. This limited our ability to detect meaningful associations in these subgroups of patients, to include whether or not combination therapy influenced their clinical outcome. Finally, we did not examine the exact timing of antibiotic therapy relative to the onset of severe sepsis. Instead we used a 12‐hour window from when subsequently positive blood cultures were drawn to the administration of initial antibiotic therapy. Other investigators have shown that delays in initial appropriate therapy of more than one hour for patients with septic shock increases the risk of death.9, 30 Failure to include the exact timing of therapy could have resulted in a final multivariate model that includes prediction variables that would not otherwise have been incorporated.

In summary, we demonstrated that resistance to the initial antibiotic treatment regimen was associated with a greater risk of hospital mortality in patients with severe sepsis attributed to Gram‐negative bacteremia. These findings imply that more rapid assessment of antimicrobial susceptibility could result in improved prescription of antibiotics in order to maximize initial administration of appropriate therapy. Future studies are required to address whether rapid determination of antimicrobial susceptibility can result in more effective administration of appropriate therapy, and if this can result in improved patient outcomes.

Severe sepsis and septic shock are associated with excess mortality when inappropriate initial antimicrobial therapy, defined as an antimicrobial regimen that lacks in vitro activity against the isolated organism(s) responsible for the infection, is administered.14 Unfortunately, bacterial resistance to antibiotics is increasing and creates a therapeutic challenge for clinicians when treating patients with serious infections, such as severe sepsis. Increasing rates of bacterial resistance leads many clinicians to empirically treat critically ill patients with broad‐spectrum antibiotics, which can perpetuate the cycle of increasing resistance.5, 6 Conversely, inappropriate initial antimicrobial therapy can lead to treatment failures and adverse patient outcomes.7 Individuals with severe sepsis appear to be at particularly high risk of excess mortality when inappropriate initial antimicrobial therapy is administered.8, 9

The most recent Surviving Sepsis Guidelines recommend empiric combination therapy targeting Gram‐negative bacteria, particularly for patients with known or suspected Pseudomonas infections, as a means to decrease the likelihood of administering inappropriate initial antimicrobial therapy.10 However, the selection of an antimicrobial regimen that is active against the causative pathogen(s) is problematic, as the treating physician usually does not know the susceptibilities of the pathogen(s) for the selected empiric antibiotics. Therefore, we performed a study with the main goal of determining whether resistance to the initially prescribed antimicrobial regimen was associated with clinical outcome in patients with severe sepsis attributed to Gram‐negative bacteremia.

Materials and Methods

Results

Antimicrobial Treatment and Resistance

Among the study patients, 358 (66.9%) received cefepime, 102 (19.1%) received piperacillin‐tazobactam, and 75 (14.0%) received a carbapenem (meropenem or imipenem) as their initial antibiotic treatment. There were 169 (31.6%) patients who received initial combination therapy with either an aminoglycoside (n = 99, 58.6%) or ciprofloxacin (n = 70, 41.4%). Eighty‐two (15.3%) patients were infected with a pathogen that was resistant to the initial antibiotic treatment regimen [cefepime (n = 41; 50.0%), piperacillin‐tazobactam (n = 25; 30.5%), or imipenem/meropenem (n = 16; 19.5%), plus either an aminoglycoside or ciprofloxacin (n = 28; 34.1%)], and were classified as receiving inappropriate initial antibiotic therapy. Among the 453 (84.7%) patients infected with a pathogen that was susceptible to the initial antibiotic regimen, there was no relationship identified between minimum inhibitory concentration values and hospital mortality.

Patients infected with a pathogen resistant to the initial antibiotic regimen had significantly greater risk of hospital mortality (63.4% vs 40.0%; P < 0.001) (Figure 1). For the 82 individuals infected with a pathogen that was resistant to the initial antibiotic regimen, no difference in hospital mortality was observed among those prescribed initial combination treatment with an aminoglycoside (n = 17) (64.7% vs 61.1%; P = 0.790) or ciprofloxacin (n = 11) (72.7% vs 61.1%; P = 0.733) compared to monotherapy (n = 54). Similarly, among the patients infected with a pathogen that was susceptible to the initial antibiotic regimen, there was no difference in hospital mortality among those whose bloodstream isolate was only susceptible to the prescribed aminoglycoside (n = 12) compared to patients with isolates that were susceptible to the prescribed beta‐lactam antibiotic (n = 441) (41.7% vs 39.9%; P = 0.902).

Figure 1

Logistic regression analysis identified infection with a pathogen resistant to the initial antibiotic regimen [adjusted odds ratio (AOR), 2.28; 95% confidence interval (CI), 1.69‐3.08; P = 0.006], increasing APACHE II scores (1‐point increments) (AOR, 1.13; 95% CI, 1.10‐1.15; P < 0.001), the need for vasopressors (AOR, 2.57; 95% CI, 2.15‐3.53; P < 0.001), the need for mechanical ventilation (AOR, 2.54; 95% CI, 2.19‐3.47; P < 0.001), healthcare‐associated hospital‐onset infection (AOR, 1.67; 95% CI, 1.32‐2.10; P =0.027), and infection with Pseudomonas aeruginosa (AOR, 2.21; 95% CI, 1.74‐2.86; P =0.002) as independent risk factors for hospital mortality (Hosmer‐Lemeshow goodness‐of‐fit test = 0.305). The model explained between 29.7% (Cox and Snell R square) and 39.8% (Nagelkerke R squared) of the variance in hospital mortality, and correctly classified 75.3% of cases.

Discussion

Our study demonstrated that hospital nonsurvivors with severe sepsis attributed to Gram‐negative bacteremia had significantly greater rates of resistance to their initially prescribed antibiotic regimen compared to hospital survivors. This observation was confirmed in a multivariate analysis controlling for severity of illness and other potential confounding variables. Additionally, acquired organ system derangements and hospital length of stay were greater for patients infected with Gram‐negative pathogens resistant to the empiric antibiotic regimen. We also observed no survival advantage with the use of combination antimicrobial therapy for the subgroup of patients whose pathogens were resistant to the initially prescribed antibiotic regimen. Lastly, no difference in mortality was observed for patients with bacterial isolates that were susceptible only to the prescribed aminoglycoside compared to those with isolates susceptible to the prescribed beta‐lactam antibiotic.

Several previous investigators have linked antibiotic resistance and outcome in patients with serious infections attributed to Gram‐negative bacteria. Tam et al. examined 34 patients with Pseudomonas aeruginosa bacteremia having elevated MICs to piperacillin‐tazobactam (32 g/mL) that were reported as susceptible.19 In seven of these cases, piperacillin‐tazobactam was prescribed empirically, whereas other agents directed against Gram‐negative bacteria were employed in the other patients (carbapenems, aminoglycosides). Thirty‐day mortality was significantly greater for the patients treated with piperacillin‐tazobactam (85.7% vs 22.2%; P = 0.004), and a multivariate analysis found treatment with piperacillin‐tazobactam to be independently associated with 30‐day mortality. Similarly, Bhat et al. examined 204 episodes of bacteremia caused by Gram‐negative bacteria for which patients received cefepime.20 Patients infected with a Gram‐negative bacteria having an MIC to cefepime greater than, or equal to, 8 g/mL had a significantly greater 28‐day mortality compared to patients infected with isolates having an MIC to cefepime that was less than 8 g/mL (54.8% vs 24.1%; P = 0.001).

Our findings are consistent with earlier studies of patients with serious Gram‐negative infections including bacteremia and nosocomial pneumonia. Micek et al. showed that patients with Pseudomonas aeruginosa bacteremia who received inappropriate initial antimicrobial therapy had a greater risk of hospital mortality compared to patients initially treated with an antimicrobial regimen having activity for the Pseudomonas isolate based on in vitro susceptibility testing.21 Similarly, Trouillet et al.,22 Beardsley et al.,23 and Heyland et al.24 found that combination antimicrobial regimens directed against Gram‐negative bacteria in patients with nosocomial pneumonia were more likely to be appropriate based on the antimicrobial susceptibility patterns of the organisms compared to monotherapy. In a more recent study, Micek et al. demonstrated that combination antimicrobial therapy directed against severe sepsis attributed to Gram‐negative bacteria was associated with improved outcomes compared to monotherapy, especially when the combination agent was an aminoglycoside.25 However, empiric combination therapy that included an aminoglycoside was also associated with increased nephrotoxicity which makes the empiric use of aminoglycosides in all patients with suspected Gram‐negative severe sepsis problematic.25, 26 Nevertheless, the use of combination therapy represents a potential strategy to maximize the administration of appropriate treatment for serious Gram‐negative bacterial infections.

Rapid assessment of antimicrobial susceptibility is another strategy that offers the possibility of identifying the resistance pattern of Gram‐negative pathogens quickly in order to provide more appropriate treatment. Bouza et al. found that use of a rapid E‐test on the respiratory specimens of patients with ventilator‐associated pneumonia was associated with fewer days of fever, fewer days of antibiotic administration until resolution of the episode of ventilator‐associated pneumonia, decreased antibiotic consumption, less Clostridium difficile‐associated diarrhea, lower costs of antimicrobial agents, and fewer days receiving mechanical ventilation.27 Other methods for the rapid identification of resistant bacteria include real‐time polymerase chain reaction assays based on hybridization probes to identify specific resistance mechanisms in bacteria.28 Application of such methods for identification of broad categories of resistance mechanisms in Gram‐negative bacteria offer the possibility of tailoring initial antimicrobial regimens in order to provide appropriate therapy in a more timely manner.

Our study has several important limitations that should be noted. First, the study was performed at a single center and the results may not be generalizable to other institutions. However, the findings from other investigators corroborate the importance of antimicrobial resistance as a predictor of outcome for patients with serious Gram‐negative infections.19, 20 Additionally, a similar association has been observed in patients with methicillin‐resistant Staphylococcus aureus bacteremia, supporting the more general importance of antimicrobial resistance as an outcome predictor.29 Second, the method employed for determining MICs was a literature‐based linear regression method correlating disk diffusion diameters with broth dilution MIC determinations. Therefore, the lack of correlation we observed between MIC values and outcome for susceptible Gram‐negative isolates associated with severe sepsis requires further confirmation. Third, we only examined 3 antibiotics, or antibiotic classes, so our results may not be applicable to other agents. This also applies to doripenem, as we did not have that specific carbapenem available at the time this investigation took place.

Another important limitation of our study is the relatively small number of individuals infected with a pathogen that was resistant to the initial treatment regimen, or only susceptible to the aminoglycoside when combination therapy was prescribed. This limited our ability to detect meaningful associations in these subgroups of patients, to include whether or not combination therapy influenced their clinical outcome. Finally, we did not examine the exact timing of antibiotic therapy relative to the onset of severe sepsis. Instead we used a 12‐hour window from when subsequently positive blood cultures were drawn to the administration of initial antibiotic therapy. Other investigators have shown that delays in initial appropriate therapy of more than one hour for patients with septic shock increases the risk of death.9, 30 Failure to include the exact timing of therapy could have resulted in a final multivariate model that includes prediction variables that would not otherwise have been incorporated.

In summary, we demonstrated that resistance to the initial antibiotic treatment regimen was associated with a greater risk of hospital mortality in patients with severe sepsis attributed to Gram‐negative bacteremia. These findings imply that more rapid assessment of antimicrobial susceptibility could result in improved prescription of antibiotics in order to maximize initial administration of appropriate therapy. Future studies are required to address whether rapid determination of antimicrobial susceptibility can result in more effective administration of appropriate therapy, and if this can result in improved patient outcomes.

In personal terms, all Americans are connected by recollections of the experience. 97% can remember exactly where they were or what they were doing the moment they heard about the attacks. (Pew Research survey, September 5, 2002)

The classic flashbulb memories of our parents' generation, who were young adults in the 1960s, were the assassinations of Martin Luther King and President John Kennedy. In the same way, the 9/11 attacks seem destined to endure as our generation's flashbulb memory, with the Space Shuttle Challenger explosion a distant second for those of us on the far side of age 40. Few of us are likely to ever forget the grief, anger, and confusion of September 11, 2001 and the days that followed, and it seems appropriate 10 years later to remember those who died that day, and to reflect on the lessons we learnedor should have. As hospitalists, we are at least somewhat familiar with the tragic and senseless loss of life that day, as the terrorist attacks were in a sense a reflection, writ large, of the unexpected and inexplicable deaths we have all been a part of: the healthy young woman exsanguinating from DIC in the immediate postpartum period, the preschool teacher rapidly succumbing to pneumococcal meningitis, the young adult dying of acute leukemia or necrotizing fasciitis.

On September 11, 2001, one of us (B.J.H.) was 2 years out of residency and in private practice near San Francisco.

For the other of us (J.C.P.), the news came in a patient's room during rounds.

What lessons should we take away from the 9/11 tragedy a decade later, and indeed from our work with our patients? Certainly that mass casualties and disaster preparedness are an unfortunate fact of life in the 21st century, and that hospitalists have a responsibility to engage with our institutions in preparing for these eventualities. Possibly that life is uncertain and, at best, goes by much more quickly than any of us could have imagined when we embarked on our medical training. In the end, that our lives are measured primarily not by the number of years we live, but by how we live them, and the lives that we touch along the way.

Once a year, we pause to remember the nearly 3000 individuals who lost their lives on 9/11. As hospitalists, we practice a profession that demands a great deal from us and encourages workaholism; perhaps the 10th anniversary of those heinous acts should make each of us, as we remember the lives touched most directly by the attacks on the World Trade Center, the Pentagon, and United Flight 93, also pause to consider our work‐life balance, and to ensure that we are reserving sufficient quality time for our families and friends, as well as for activities that renew and enrich us.

In personal terms, all Americans are connected by recollections of the experience. 97% can remember exactly where they were or what they were doing the moment they heard about the attacks. (Pew Research survey, September 5, 2002)

The classic flashbulb memories of our parents' generation, who were young adults in the 1960s, were the assassinations of Martin Luther King and President John Kennedy. In the same way, the 9/11 attacks seem destined to endure as our generation's flashbulb memory, with the Space Shuttle Challenger explosion a distant second for those of us on the far side of age 40. Few of us are likely to ever forget the grief, anger, and confusion of September 11, 2001 and the days that followed, and it seems appropriate 10 years later to remember those who died that day, and to reflect on the lessons we learnedor should have. As hospitalists, we are at least somewhat familiar with the tragic and senseless loss of life that day, as the terrorist attacks were in a sense a reflection, writ large, of the unexpected and inexplicable deaths we have all been a part of: the healthy young woman exsanguinating from DIC in the immediate postpartum period, the preschool teacher rapidly succumbing to pneumococcal meningitis, the young adult dying of acute leukemia or necrotizing fasciitis.

On September 11, 2001, one of us (B.J.H.) was 2 years out of residency and in private practice near San Francisco.

For the other of us (J.C.P.), the news came in a patient's room during rounds.

What lessons should we take away from the 9/11 tragedy a decade later, and indeed from our work with our patients? Certainly that mass casualties and disaster preparedness are an unfortunate fact of life in the 21st century, and that hospitalists have a responsibility to engage with our institutions in preparing for these eventualities. Possibly that life is uncertain and, at best, goes by much more quickly than any of us could have imagined when we embarked on our medical training. In the end, that our lives are measured primarily not by the number of years we live, but by how we live them, and the lives that we touch along the way.

Once a year, we pause to remember the nearly 3000 individuals who lost their lives on 9/11. As hospitalists, we practice a profession that demands a great deal from us and encourages workaholism; perhaps the 10th anniversary of those heinous acts should make each of us, as we remember the lives touched most directly by the attacks on the World Trade Center, the Pentagon, and United Flight 93, also pause to consider our work‐life balance, and to ensure that we are reserving sufficient quality time for our families and friends, as well as for activities that renew and enrich us.

In personal terms, all Americans are connected by recollections of the experience. 97% can remember exactly where they were or what they were doing the moment they heard about the attacks. (Pew Research survey, September 5, 2002)

The classic flashbulb memories of our parents' generation, who were young adults in the 1960s, were the assassinations of Martin Luther King and President John Kennedy. In the same way, the 9/11 attacks seem destined to endure as our generation's flashbulb memory, with the Space Shuttle Challenger explosion a distant second for those of us on the far side of age 40. Few of us are likely to ever forget the grief, anger, and confusion of September 11, 2001 and the days that followed, and it seems appropriate 10 years later to remember those who died that day, and to reflect on the lessons we learnedor should have. As hospitalists, we are at least somewhat familiar with the tragic and senseless loss of life that day, as the terrorist attacks were in a sense a reflection, writ large, of the unexpected and inexplicable deaths we have all been a part of: the healthy young woman exsanguinating from DIC in the immediate postpartum period, the preschool teacher rapidly succumbing to pneumococcal meningitis, the young adult dying of acute leukemia or necrotizing fasciitis.

On September 11, 2001, one of us (B.J.H.) was 2 years out of residency and in private practice near San Francisco.

For the other of us (J.C.P.), the news came in a patient's room during rounds.

What lessons should we take away from the 9/11 tragedy a decade later, and indeed from our work with our patients? Certainly that mass casualties and disaster preparedness are an unfortunate fact of life in the 21st century, and that hospitalists have a responsibility to engage with our institutions in preparing for these eventualities. Possibly that life is uncertain and, at best, goes by much more quickly than any of us could have imagined when we embarked on our medical training. In the end, that our lives are measured primarily not by the number of years we live, but by how we live them, and the lives that we touch along the way.

Once a year, we pause to remember the nearly 3000 individuals who lost their lives on 9/11. As hospitalists, we practice a profession that demands a great deal from us and encourages workaholism; perhaps the 10th anniversary of those heinous acts should make each of us, as we remember the lives touched most directly by the attacks on the World Trade Center, the Pentagon, and United Flight 93, also pause to consider our work‐life balance, and to ensure that we are reserving sufficient quality time for our families and friends, as well as for activities that renew and enrich us.

Hospital readmissions have become a focus of national attention as a potential indicator of poor quality and health care waste.13 Geographic variations in readmission rates, a high rate of unplanned readmissions, and the emergence of promising interventions all suggest that some portion of readmissions are preventable.4, 5 This work adds to the work of the Agency for Healthcare Research and Quality (AHRQ) on reports of preventable hospital admissions, using hospitalization rates for ambulatory‐sensitive conditions as prevention quality indicators.6

The actual proportion of preventable readmissions is unknown. In previous research using physician reviewers, estimates have ranged from 5% to 38%.713 More recently, studies using a methodology based on relationships between diagnoses at the initial and subsequent hospitalizations have flagged as many as 76% of 30‐day readmissions as preventable.14

Understanding the preventability of readmissions is important if we are to gauge the true size of this quality and cost opportunity. Moreover, it is important to assess the beliefs of the front‐line clinicians who will be playing key roles in prevention.

The objective of the current study was to examine readmission preventability from the perspective of hospital medicine experts practicing at a community hospital. Through detailed chart review, we identify patient factors and care processes that affect preventability and describe clinicians' ideas for preventing future readmissions.

METHODS

RESULTS

Two hundred thirteen patients (85%) had a single readmission. Another 33 patients had 2 readmissions, and 5 patients accounted for 21 readmissions for a total sample of 300 cases. Table 1 provides characteristics of readmitted patients. They were likely to be elderly; the mean (SD) age was 75.3 (15.3), and more than 48% were 80 or older. Sixty‐six percent of patients were taking more than ten medications, and a quarter (25%) had more than three new medications prescribed at discharge. Frequent diagnoses at the index admission included renal insufficiency, heart failure, dementia, atrial fibrillation, and chronic obstructive pulmonary disease (COPD). The majority of cases had more than one diagnosis identified at their first admission. These diagnoses are what hospitalists believe are significant patient issues rather than the hospital‐coded principal and secondary diagnoses.

Sixty‐four percent readmitted cases had been discharged to home (including those with home services), and 36% were discharged to a care facility (skilled nursing facility [SNF], foster care, assisted living) (Table 2). Fifty‐eight percent of cases were readmitted within seven days of the index admission, and another 29% within the first two weeks. Exactly 75% of the time, the readmission was for the same or related diagnosis as the index admission. Primary care follow‐up did not occur as recommended 69% of the time, and 57% of the time the patient was readmitted prior seeing their primary care physician (PCP).

Overall, only 15% of readmissions were termed preventable by the hospital reviewers, although another 46% were deemed possibly preventable. Preventability ratings varied by reviewer, ranging from a high of 27% to a low of 0% among hospitalists rating ten or more cases (Table 3). There was similar variation in the number of recommended interventions. For readmissions deemed preventable or possibly preventable, the number of potential interventions ranged from more than three per patient to less than one per patient.

The most frequently mentioned intervention that could have prevented a readmission was to extend the hospital stay by one to two days (Table 4). An earlier PCP appointment was suggested for another 21% of readmissions. Other interventions received a scattering of mentions. The types of recommended interventions varied with the rater's perception of preventability (Figure 1, available online). Hospitalists were more likely to recommend a longer initial stay, medication changes, or additional education at discharge, and earlier contact from a care facility, for readmissions they thought were preventable. For possibly preventable readmissions, these same recommendations were important, but hospitalists were also likely to recommend case management, disposition to a higher level of care, or a home health visit.

Table 5 shows the most important characteristics associated with preventability, using a cutoff of 0.2 in statistical significance. Readmissions for the same diagnosis were more likely than others to be rated preventable, as were cases with a short readmission interval, more than three new medications at discharge, and patients with COPD or depression/anxiety. Initial hospital length of stay did not influence preventability, nor did it influence the likelihood of a reviewer recommending a longer initial stay.

Potential predictors associated with preventability were included in a hierarchical logistic regression model, with hospital site and reviewer included as random effects. In this modeling, preventable readmissions were more likely than nonpreventable readmissions to be influenced by three process factors: having the same index and readmission diagnosis; readmission in the first post‐hospital week; being readmitted prior to a primary care follow‐up; and three patient factors: having more than three new discharge medications, having anticoagulation treatment, and having a COPD diagnosis (data available online). Other chronic diseases, age, discharge location, or previous readmissions were not important in the rating of preventability. When entered as random effects in a hierarchical logistic regression model, the categorical variable representing hospital site did not significantly improve prediction (P = 0.42), but the reviewer variable (categorized by the top six reviewers and others) had marginal significance at P = 0.088.

DISCUSSION

Reported high Medicare 30‐day readmission rates and associate excess costs have created a national climate for eliminating unnecessary hospital readmissions.1 Recently passed healthcare legislation in the USA will put in place diagnosis‐related group (DRG) payment reductions for excess readmission rates by 2013. As the definitions and methodologies for determining the relatedness and preventable nature of readmissions continues to be clarified, this study contributes to the understanding of preventability and specific preventative strategies from a physician perspective. Although potential savings in readmission reduction work is attractive, our study indicates that most front‐line clinicians are not convinced that a large portion of readmissions are preventable.

The proportion of preventable readmissions found in our study is very much in line with previous research.713 Certain predictors of preventable readmissions were also similar. Several researchers have found that preventable readmissions are more likely to be early,8, 10, 12 and have the same or related diagnosis as the initial stay.8 On the other hand, our data did not show an independent effect of age on preventability, as others have suggested.9, 17 Patients with a large number of diagnoses and medications have been shown to be at risk for preventable readmissions,9 but the importance of new discharge medications has not been widely researched and is a factor that deserves further exploration.

One key message from our study was found in the variation in the ratings of preventability by individual physicians. At first blush, it may appear to reflect a lack of inter‐rater reliability or understanding of the underlying concept of preventability. We believe this is unlikely, given the discussions among raters and the clear descriptions offered in writing. Moreover, there was much less variation in other judgments such as the ratings of relatedness of the readmission diagnosis (chi‐square = 21.7, P = .041)

There are a number of possible reasons for variation in reviewer ratings of preventability. Reviewers did vary with regard to age, experience, tenure in the organization, gender, and full/part‐time status. They practiced at different hospitals. None of these factors were related to ratings of preventability. On the other hand, three explanations are worth noting.

First, the hierarchical regression models found that reviewer only slightly improved prediction (P = 0.088), above and beyond the other diagnosis and process factors. This would lead us to reject the factor of reviewer as the most important predictor of preventability; the other case characteristics mentioned above were more important.

Second, the three hospitalists who were more optimistic (rated more cases as preventable) reviewed more charts than others. It is possible that these three were more engaged, not only in the chart review process, but more eager to uncover potential remedies to prevent readmissions. While generating more ideas about how to do that, they rated more readmissions as preventable. We do not believe that actually doing more reviews caused them to rate a greater portion as preventable; none of the reviewers showed progression to more preventable ratings over time (analysis not shown).

Finally, it is worth noting that two of the more optimistic physicians had previous primary care experience. This is an intriguing explanation that would benefit from further research. First‐hand experience with primary care case management, rapid appointment follow‐up, home service referrals, and the like may give the practicing hospitalist reason to believe that actions in the ambulatory setting can prevent readmissions.

Regardless of the source, the variation demonstrates cultural or philosophical biases among clinicians regarding how much influence additional planning, education, and care coordination can have on readmissions. We believe that this variation must be addressed in the implementation of readmission reduction programs. Physician engagement will be more likely if there is optimism about the potential to prevent readmissions. In addition, it will be important to develop more consensus about effective interventions from the perspectives of hospital physicians, primary care physicians, nurses, and patients, as others have alluded.18, 19

The significant rate of related readmissions (75%) has implications for the potential Centers for Medicare and Medicaid Services (CMS) methodology that will be used to reduce DRG payments, given the legislation's current intent to exclude only unrelated and planned readmissions from the calculations. Providing clear definitions on relatedness and a methodology to code this criterion in administrative datasets may need to be developed. The views of hospitalists in the current study suggest that the relatedness methodology may be overly sensitive and not yet specific enough to isolate truly preventable readmissions. Less than a quarter of related readmissions were deemed preventable by these raters.

Hospitalists found both patient and process factors important in assessing the preventability of a readmission. This kind of analysis can point to subgroups with potential for targeted intervention. For example, over a third of patients readmitted within a week for the same diagnosis were rated as preventable, indicating a critical follow‐up period for some patients. Higher ratings of preventability among the readmissions for patients on anticoagulation or who were given more than three new medications at discharge indicates that better medication management may indeed be a fruitful strategy for readmission reduction.

The finding that increasing the length of the initial hospital stay was rated as the most prevalent strategy to mitigate against readmission in our retrospective review was surprising. It emphasizes the tension between efficient hospital throughput which reduces unnecessary hospital days and the necessity for appropriate monitoring to ensure clinical stability prior to discharge. Excess hospital days can prolong the exposure to a multitude of hospital acquired conditions (HAC), and this risk must be weighed against a longer length of stay and the time required delivering the appropriate hospital services.

Exploring alternative strategies to reduce readmissions without increasing the hospital length of stay is a reasonable response to this tension. Better discharge education and attention to discharge medications and dosages were also recommended strategies for preventable readmissions. These are interventions hospitalists are familiar with and can control. Relatively smaller percentages of patients were thought to benefit from case management, hospice, home health, or an MD visit to their nursing home, and hospitalists were more likely to recommend these for the possibly preventable patients. These interventions are not fully implemented within the study health system so there is understandably less confidence in them.

Limitations of this study include its relatively small sample size and the fact that all patients were served by a single medical practice. No extensive inter‐rater reliability checks were performed, although all reviewers were trained in the definitions of the most important judgment items. Other limitations include possible confounding biases which were not controlled, such as the number of charts reviewed, timing of review, and hospital reviewed (ie, each reviewer did not review the same proportion of charts from each hospital).

SUMMARY

We have presented a retrospective chart review study of hospital readmissions in a community hospital setting. This study adds to the increasing literature describing the factors that contribute to hospital readmissions, how preventable they are, and what strategies may reduce the likelihood of readmission. This study is unique in its contribution to the understanding of hospital readmissions by studying front‐line clinician (hospitalist) perceptions of those factors.

Hospital readmissions have become a focus of national attention as a potential indicator of poor quality and health care waste.13 Geographic variations in readmission rates, a high rate of unplanned readmissions, and the emergence of promising interventions all suggest that some portion of readmissions are preventable.4, 5 This work adds to the work of the Agency for Healthcare Research and Quality (AHRQ) on reports of preventable hospital admissions, using hospitalization rates for ambulatory‐sensitive conditions as prevention quality indicators.6

The actual proportion of preventable readmissions is unknown. In previous research using physician reviewers, estimates have ranged from 5% to 38%.713 More recently, studies using a methodology based on relationships between diagnoses at the initial and subsequent hospitalizations have flagged as many as 76% of 30‐day readmissions as preventable.14

Understanding the preventability of readmissions is important if we are to gauge the true size of this quality and cost opportunity. Moreover, it is important to assess the beliefs of the front‐line clinicians who will be playing key roles in prevention.

The objective of the current study was to examine readmission preventability from the perspective of hospital medicine experts practicing at a community hospital. Through detailed chart review, we identify patient factors and care processes that affect preventability and describe clinicians' ideas for preventing future readmissions.

METHODS

RESULTS

Two hundred thirteen patients (85%) had a single readmission. Another 33 patients had 2 readmissions, and 5 patients accounted for 21 readmissions for a total sample of 300 cases. Table 1 provides characteristics of readmitted patients. They were likely to be elderly; the mean (SD) age was 75.3 (15.3), and more than 48% were 80 or older. Sixty‐six percent of patients were taking more than ten medications, and a quarter (25%) had more than three new medications prescribed at discharge. Frequent diagnoses at the index admission included renal insufficiency, heart failure, dementia, atrial fibrillation, and chronic obstructive pulmonary disease (COPD). The majority of cases had more than one diagnosis identified at their first admission. These diagnoses are what hospitalists believe are significant patient issues rather than the hospital‐coded principal and secondary diagnoses.

Sixty‐four percent readmitted cases had been discharged to home (including those with home services), and 36% were discharged to a care facility (skilled nursing facility [SNF], foster care, assisted living) (Table 2). Fifty‐eight percent of cases were readmitted within seven days of the index admission, and another 29% within the first two weeks. Exactly 75% of the time, the readmission was for the same or related diagnosis as the index admission. Primary care follow‐up did not occur as recommended 69% of the time, and 57% of the time the patient was readmitted prior seeing their primary care physician (PCP).

Overall, only 15% of readmissions were termed preventable by the hospital reviewers, although another 46% were deemed possibly preventable. Preventability ratings varied by reviewer, ranging from a high of 27% to a low of 0% among hospitalists rating ten or more cases (Table 3). There was similar variation in the number of recommended interventions. For readmissions deemed preventable or possibly preventable, the number of potential interventions ranged from more than three per patient to less than one per patient.

The most frequently mentioned intervention that could have prevented a readmission was to extend the hospital stay by one to two days (Table 4). An earlier PCP appointment was suggested for another 21% of readmissions. Other interventions received a scattering of mentions. The types of recommended interventions varied with the rater's perception of preventability (Figure 1, available online). Hospitalists were more likely to recommend a longer initial stay, medication changes, or additional education at discharge, and earlier contact from a care facility, for readmissions they thought were preventable. For possibly preventable readmissions, these same recommendations were important, but hospitalists were also likely to recommend case management, disposition to a higher level of care, or a home health visit.

Table 5 shows the most important characteristics associated with preventability, using a cutoff of 0.2 in statistical significance. Readmissions for the same diagnosis were more likely than others to be rated preventable, as were cases with a short readmission interval, more than three new medications at discharge, and patients with COPD or depression/anxiety. Initial hospital length of stay did not influence preventability, nor did it influence the likelihood of a reviewer recommending a longer initial stay.

Potential predictors associated with preventability were included in a hierarchical logistic regression model, with hospital site and reviewer included as random effects. In this modeling, preventable readmissions were more likely than nonpreventable readmissions to be influenced by three process factors: having the same index and readmission diagnosis; readmission in the first post‐hospital week; being readmitted prior to a primary care follow‐up; and three patient factors: having more than three new discharge medications, having anticoagulation treatment, and having a COPD diagnosis (data available online). Other chronic diseases, age, discharge location, or previous readmissions were not important in the rating of preventability. When entered as random effects in a hierarchical logistic regression model, the categorical variable representing hospital site did not significantly improve prediction (P = 0.42), but the reviewer variable (categorized by the top six reviewers and others) had marginal significance at P = 0.088.

DISCUSSION

Reported high Medicare 30‐day readmission rates and associate excess costs have created a national climate for eliminating unnecessary hospital readmissions.1 Recently passed healthcare legislation in the USA will put in place diagnosis‐related group (DRG) payment reductions for excess readmission rates by 2013. As the definitions and methodologies for determining the relatedness and preventable nature of readmissions continues to be clarified, this study contributes to the understanding of preventability and specific preventative strategies from a physician perspective. Although potential savings in readmission reduction work is attractive, our study indicates that most front‐line clinicians are not convinced that a large portion of readmissions are preventable.

The proportion of preventable readmissions found in our study is very much in line with previous research.713 Certain predictors of preventable readmissions were also similar. Several researchers have found that preventable readmissions are more likely to be early,8, 10, 12 and have the same or related diagnosis as the initial stay.8 On the other hand, our data did not show an independent effect of age on preventability, as others have suggested.9, 17 Patients with a large number of diagnoses and medications have been shown to be at risk for preventable readmissions,9 but the importance of new discharge medications has not been widely researched and is a factor that deserves further exploration.

One key message from our study was found in the variation in the ratings of preventability by individual physicians. At first blush, it may appear to reflect a lack of inter‐rater reliability or understanding of the underlying concept of preventability. We believe this is unlikely, given the discussions among raters and the clear descriptions offered in writing. Moreover, there was much less variation in other judgments such as the ratings of relatedness of the readmission diagnosis (chi‐square = 21.7, P = .041)

There are a number of possible reasons for variation in reviewer ratings of preventability. Reviewers did vary with regard to age, experience, tenure in the organization, gender, and full/part‐time status. They practiced at different hospitals. None of these factors were related to ratings of preventability. On the other hand, three explanations are worth noting.

First, the hierarchical regression models found that reviewer only slightly improved prediction (P = 0.088), above and beyond the other diagnosis and process factors. This would lead us to reject the factor of reviewer as the most important predictor of preventability; the other case characteristics mentioned above were more important.

Second, the three hospitalists who were more optimistic (rated more cases as preventable) reviewed more charts than others. It is possible that these three were more engaged, not only in the chart review process, but more eager to uncover potential remedies to prevent readmissions. While generating more ideas about how to do that, they rated more readmissions as preventable. We do not believe that actually doing more reviews caused them to rate a greater portion as preventable; none of the reviewers showed progression to more preventable ratings over time (analysis not shown).

Finally, it is worth noting that two of the more optimistic physicians had previous primary care experience. This is an intriguing explanation that would benefit from further research. First‐hand experience with primary care case management, rapid appointment follow‐up, home service referrals, and the like may give the practicing hospitalist reason to believe that actions in the ambulatory setting can prevent readmissions.

Regardless of the source, the variation demonstrates cultural or philosophical biases among clinicians regarding how much influence additional planning, education, and care coordination can have on readmissions. We believe that this variation must be addressed in the implementation of readmission reduction programs. Physician engagement will be more likely if there is optimism about the potential to prevent readmissions. In addition, it will be important to develop more consensus about effective interventions from the perspectives of hospital physicians, primary care physicians, nurses, and patients, as others have alluded.18, 19

The significant rate of related readmissions (75%) has implications for the potential Centers for Medicare and Medicaid Services (CMS) methodology that will be used to reduce DRG payments, given the legislation's current intent to exclude only unrelated and planned readmissions from the calculations. Providing clear definitions on relatedness and a methodology to code this criterion in administrative datasets may need to be developed. The views of hospitalists in the current study suggest that the relatedness methodology may be overly sensitive and not yet specific enough to isolate truly preventable readmissions. Less than a quarter of related readmissions were deemed preventable by these raters.

Hospitalists found both patient and process factors important in assessing the preventability of a readmission. This kind of analysis can point to subgroups with potential for targeted intervention. For example, over a third of patients readmitted within a week for the same diagnosis were rated as preventable, indicating a critical follow‐up period for some patients. Higher ratings of preventability among the readmissions for patients on anticoagulation or who were given more than three new medications at discharge indicates that better medication management may indeed be a fruitful strategy for readmission reduction.

The finding that increasing the length of the initial hospital stay was rated as the most prevalent strategy to mitigate against readmission in our retrospective review was surprising. It emphasizes the tension between efficient hospital throughput which reduces unnecessary hospital days and the necessity for appropriate monitoring to ensure clinical stability prior to discharge. Excess hospital days can prolong the exposure to a multitude of hospital acquired conditions (HAC), and this risk must be weighed against a longer length of stay and the time required delivering the appropriate hospital services.

Exploring alternative strategies to reduce readmissions without increasing the hospital length of stay is a reasonable response to this tension. Better discharge education and attention to discharge medications and dosages were also recommended strategies for preventable readmissions. These are interventions hospitalists are familiar with and can control. Relatively smaller percentages of patients were thought to benefit from case management, hospice, home health, or an MD visit to their nursing home, and hospitalists were more likely to recommend these for the possibly preventable patients. These interventions are not fully implemented within the study health system so there is understandably less confidence in them.

Limitations of this study include its relatively small sample size and the fact that all patients were served by a single medical practice. No extensive inter‐rater reliability checks were performed, although all reviewers were trained in the definitions of the most important judgment items. Other limitations include possible confounding biases which were not controlled, such as the number of charts reviewed, timing of review, and hospital reviewed (ie, each reviewer did not review the same proportion of charts from each hospital).

SUMMARY

We have presented a retrospective chart review study of hospital readmissions in a community hospital setting. This study adds to the increasing literature describing the factors that contribute to hospital readmissions, how preventable they are, and what strategies may reduce the likelihood of readmission. This study is unique in its contribution to the understanding of hospital readmissions by studying front‐line clinician (hospitalist) perceptions of those factors.

Hospital readmissions have become a focus of national attention as a potential indicator of poor quality and health care waste.13 Geographic variations in readmission rates, a high rate of unplanned readmissions, and the emergence of promising interventions all suggest that some portion of readmissions are preventable.4, 5 This work adds to the work of the Agency for Healthcare Research and Quality (AHRQ) on reports of preventable hospital admissions, using hospitalization rates for ambulatory‐sensitive conditions as prevention quality indicators.6

The actual proportion of preventable readmissions is unknown. In previous research using physician reviewers, estimates have ranged from 5% to 38%.713 More recently, studies using a methodology based on relationships between diagnoses at the initial and subsequent hospitalizations have flagged as many as 76% of 30‐day readmissions as preventable.14

Understanding the preventability of readmissions is important if we are to gauge the true size of this quality and cost opportunity. Moreover, it is important to assess the beliefs of the front‐line clinicians who will be playing key roles in prevention.

The objective of the current study was to examine readmission preventability from the perspective of hospital medicine experts practicing at a community hospital. Through detailed chart review, we identify patient factors and care processes that affect preventability and describe clinicians' ideas for preventing future readmissions.

METHODS

RESULTS

Two hundred thirteen patients (85%) had a single readmission. Another 33 patients had 2 readmissions, and 5 patients accounted for 21 readmissions for a total sample of 300 cases. Table 1 provides characteristics of readmitted patients. They were likely to be elderly; the mean (SD) age was 75.3 (15.3), and more than 48% were 80 or older. Sixty‐six percent of patients were taking more than ten medications, and a quarter (25%) had more than three new medications prescribed at discharge. Frequent diagnoses at the index admission included renal insufficiency, heart failure, dementia, atrial fibrillation, and chronic obstructive pulmonary disease (COPD). The majority of cases had more than one diagnosis identified at their first admission. These diagnoses are what hospitalists believe are significant patient issues rather than the hospital‐coded principal and secondary diagnoses.

Sixty‐four percent readmitted cases had been discharged to home (including those with home services), and 36% were discharged to a care facility (skilled nursing facility [SNF], foster care, assisted living) (Table 2). Fifty‐eight percent of cases were readmitted within seven days of the index admission, and another 29% within the first two weeks. Exactly 75% of the time, the readmission was for the same or related diagnosis as the index admission. Primary care follow‐up did not occur as recommended 69% of the time, and 57% of the time the patient was readmitted prior seeing their primary care physician (PCP).

Overall, only 15% of readmissions were termed preventable by the hospital reviewers, although another 46% were deemed possibly preventable. Preventability ratings varied by reviewer, ranging from a high of 27% to a low of 0% among hospitalists rating ten or more cases (Table 3). There was similar variation in the number of recommended interventions. For readmissions deemed preventable or possibly preventable, the number of potential interventions ranged from more than three per patient to less than one per patient.

The most frequently mentioned intervention that could have prevented a readmission was to extend the hospital stay by one to two days (Table 4). An earlier PCP appointment was suggested for another 21% of readmissions. Other interventions received a scattering of mentions. The types of recommended interventions varied with the rater's perception of preventability (Figure 1, available online). Hospitalists were more likely to recommend a longer initial stay, medication changes, or additional education at discharge, and earlier contact from a care facility, for readmissions they thought were preventable. For possibly preventable readmissions, these same recommendations were important, but hospitalists were also likely to recommend case management, disposition to a higher level of care, or a home health visit.

Table 5 shows the most important characteristics associated with preventability, using a cutoff of 0.2 in statistical significance. Readmissions for the same diagnosis were more likely than others to be rated preventable, as were cases with a short readmission interval, more than three new medications at discharge, and patients with COPD or depression/anxiety. Initial hospital length of stay did not influence preventability, nor did it influence the likelihood of a reviewer recommending a longer initial stay.